Literatura académica sobre el tema "ML prognostic model"
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Artículos de revistas sobre el tema "ML prognostic model"
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Uneno, Yu, Tadayuki Kou, Masashi Kanai, et al. "Prognostic model for survival in patients with advanced pancreatic cancer receiving palliative chemotherapy." Journal of Clinical Oncology 33, no. 3_suppl (2015): 248. http://dx.doi.org/10.1200/jco.2015.33.3_suppl.248.
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248 Background: The prognosis of patients with advanced pancreatic cancer (APC) is extremely poor. Several clinical and laboratory factors have been known to be associated with prognosis of APC patients. However, there are few clinically available prognostic models predicting survival in APC patients receiving palliative chemotherapy. Methods: To construct a prognostic model to predict survival in APC patients receiving palliative chemotherapy, we analyzed the clinical data from 306 consecutive patients with pathologically confirmed APC who received palliative chemotherapy. We selected six independent prognostic factors which remained independent prognostic factors after multivariate analysis. Thereafter, we rounded the regression coefficient (β) for each independent prognostic factor derived from the Cox regression equation (HR = eβ) and developed a prognostic index (PI). Results: Developed prognostic index (PI) was as follows: PI = 2 (if performance status score 2–3) + 1 (if metastatic disease) + 1 (if initially unresectable disease) + 1 (if carcinoembryonic antigen level ≥5.0 ng/ml) + 1 (if carbohydrate antigen 19-9 level ≥1000 U/ml) + 2 (if neutrophil–lymphocyte ratio ≥5). The patients were classified into three prognostic groups: favorable (PI 0–1, n = 73), intermediate (PI 2–3, n = 145), and poor prognosis (PI 4–8, n = 88). The median overall survival for each prognostic group was 16.5, 12.3 and 6.2 months, respectively, and the 1-year survival rates were 67.3%, 51.3%, and 19.1%, respectively (P < 0.01). The c index of the model was 0.658. This model was well calibrated to predict 1-year survival, in which overestimation (2.4% and 0.2% in the favorable and poor prognosis groups, respectively) and underestimation (3.6% in the intermediate prognosis group) were observed. Conclusions: This prognostic model based on readily available clinical factors would help clinicians in estimating the overall survival in APC patients receiving palliative chemotherapy.
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Martínez-Blanco, Pablo, Miguel Suárez, Sergio Gil-Rojas, et al. "Prognostic Factors for Mortality in Hepatocellular Carcinoma at Diagnosis: Development of a Predictive Model Using Artificial Intelligence." Diagnostics 14, no. 4 (2024): 406. http://dx.doi.org/10.3390/diagnostics14040406.
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Background: Hepatocellular carcinoma (HCC) accounts for 75% of primary liver tumors. Controlling risk factors associated with its development and implementing screenings in risk populations does not seem sufficient to improve the prognosis of these patients at diagnosis. The development of a predictive prognostic model for mortality at the diagnosis of HCC is proposed. Methods: In this retrospective multicenter study, the analysis of data from 191 HCC patients was conducted using machine learning (ML) techniques to analyze the prognostic factors of mortality that are significant at the time of diagnosis. Clinical and analytical data of interest in patients with HCC were gathered. Results: Meeting Milan criteria, Barcelona Clinic Liver Cancer (BCLC) classification and albumin levels were the variables with the greatest impact on the prognosis of HCC patients. The ML algorithm that achieved the best results was random forest (RF). Conclusions: The development of a predictive prognostic model at the diagnosis is a valuable tool for patients with HCC and for application in clinical practice. RF is useful and reliable in the analysis of prognostic factors in the diagnosis of HCC. The search for new prognostic factors is still necessary in patients with HCC.
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Shen, Ziyuan, Shuo Zhang, Yaxue Jiao, et al. "LASSO Model Better Predicted the Prognosis of DLBCL than Random Forest Model: A Retrospective Multicenter Analysis of HHLWG." Journal of Oncology 2022 (September 16, 2022): 1–10. http://dx.doi.org/10.1155/2022/1618272.
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Background. Diffuse large B-cell lymphoma (DLBCL) is a heterogeneous non-Hodgkin’s lymphoma with great clinical challenge. Machine learning (ML) has attracted substantial attention in diagnosis, prognosis, and treatment of diseases. This study is aimed at exploring the prognostic factors of DLBCL by ML. Methods. In total, 1211 DLBCL patients were retrieved from Huaihai Lymphoma Working Group (HHLWG). The least absolute shrinkage and selection operator (LASSO) and random forest algorithm were used to identify prognostic factors for the overall survival (OS) rate of DLBCL among twenty-five variables. Receiver operating characteristic (ROC) curve and decision curve analysis (DCA) were utilized to compare the predictive performance and clinical effectiveness of the two models, respectively. Results. The median follow-up time was 43.4 months, and the 5-year OS was 58.5%. The LASSO model achieved an Area under the curve (AUC) of 75.8% for the prognosis of DLBCL, which was higher than that of the random forest model (AUC: 71.6%). DCA analysis also revealed that the LASSO model could augment net benefits and exhibited a wider range of threshold probabilities by risk stratification than the random forest model. In addition, multivariable analysis demonstrated that age, white blood cell count, hemoglobin, central nervous system involvement, gender, and Ann Arbor stage were independent prognostic factors for DLBCL. The LASSO model showed better discrimination of outcomes compared with the IPI and NCCN-IPI models and identified three groups of patients: low risk, high-intermediate risk, and high risk. Conclusions. The prognostic model of DLBCL based on the LASSO regression was more accurate than the random forest, IPI, and NCCN-IPI models.
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Critelli, Brian, Amier Hassan, Ila Lahooti, et al. "A systematic review of machine learning-based prognostic models for acute pancreatitis: Towards improving methods and reporting quality." PLOS Medicine 22, no. 2 (2025): e1004432. https://doi.org/10.1371/journal.pmed.1004432.
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Background An accurate prognostic tool is essential to aid clinical decision-making (e.g., patient triage) and to advance personalized medicine. However, such a prognostic tool is lacking for acute pancreatitis (AP). Increasingly machine learning (ML) techniques are being used to develop high-performing prognostic models in AP. However, methodologic and reporting quality has received little attention. High-quality reporting and study methodology are critical for model validity, reproducibility, and clinical implementation. In collaboration with content experts in ML methodology, we performed a systematic review critically appraising the quality of methodology and reporting of recently published ML AP prognostic models. Methods/findings Using a validated search strategy, we identified ML AP studies from the databases MEDLINE and EMBASE published between January 2021 and December 2023. We also searched pre-print servers medRxiv, bioRxiv, and arXiv for pre-prints registered between January 2021 and December 2023. Eligibility criteria included all retrospective or prospective studies that developed or validated new or existing ML models in patients with AP that predicted an outcome following an episode of AP. Meta-analysis was considered if there was homogeneity in the study design and in the type of outcome predicted. For risk of bias (ROB) assessment, we used the Prediction Model Risk of Bias Assessment Tool. Quality of reporting was assessed using the Transparent Reporting of a Multivariable Prediction Model of Individual Prognosis or Diagnosis—Artificial Intelligence (TRIPOD+AI) statement that defines standards for 27 items that should be reported in publications using ML prognostic models. The search strategy identified 6,480 publications of which 30 met the eligibility criteria. Studies originated from China (22), the United States (4), and other (4). All 30 studies developed a new ML model and none sought to validate an existing ML model, producing a total of 39 new ML models. AP severity (23/39) or mortality (6/39) were the most common outcomes predicted. The mean area under the curve for all models and endpoints was 0.91 (SD 0.08). The ROB was high for at least one domain in all 39 models, particularly for the analysis domain (37/39 models). Steps were not taken to minimize over-optimistic model performance in 27/39 models. Due to heterogeneity in the study design and in how the outcomes were defined and determined, meta-analysis was not performed. Studies reported on only 15/27 items from TRIPOD+AI standards, with only 7/30 justifying sample size and 13/30 assessing data quality. Other reporting deficiencies included omissions regarding human–AI interaction (28/30), handling low-quality or incomplete data in practice (27/30), sharing analytical codes (25/30), study protocols (25/30), and reporting source data (19/30). Conclusions There are significant deficiencies in the methodology and reporting of recently published ML based prognostic models in AP patients. These undermine the validity, reproducibility, and implementation of these prognostic models despite their promise of superior predictive accuracy. Registration Research Registry (reviewregistry1727)
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Mirza, Zeenat, Md Shahid Ansari, Md Shahid Iqbal, et al. "Identification of Novel Diagnostic and Prognostic Gene Signature Biomarkers for Breast Cancer Using Artificial Intelligence and Machine Learning Assisted Transcriptomics Analysis." Cancers 15, no. 12 (2023): 3237. http://dx.doi.org/10.3390/cancers15123237.
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Background: Breast cancer (BC) is one of the most common female cancers. Clinical and histopathological information is collectively used for diagnosis, but is often not precise. We applied machine learning (ML) methods to identify the valuable gene signature model based on differentially expressed genes (DEGs) for BC diagnosis and prognosis. Methods: A cohort of 701 samples from 11 GEO BC microarray datasets was used for the identification of significant DEGs. Seven ML methods, including RFECV-LR, RFECV-SVM, LR-L1, SVC-L1, RF, and Extra-Trees were applied for gene reduction and the construction of a diagnostic model for cancer classification. Kaplan–Meier survival analysis was performed for prognostic signature construction. The potential biomarkers were confirmed via qRT-PCR and validated by another set of ML methods including GBDT, XGBoost, AdaBoost, KNN, and MLP. Results: We identified 355 DEGs and predicted BC-associated pathways, including kinetochore metaphase signaling, PTEN, senescence, and phagosome-formation pathways. A hub of 28 DEGs and a novel diagnostic nine-gene signature (COL10A, S100P, ADAMTS5, WISP1, COMP, CXCL10, LYVE1, COL11A1, and INHBA) were identified using stringent filter conditions. Similarly, a novel prognostic model consisting of eight-gene signatures (CCNE2, NUSAP1, TPX2, S100P, ITM2A, LIFR, TNXA, and ZBTB16) was also identified using disease-free survival and overall survival analysis. Gene signatures were validated by another set of ML methods. Finally, qRT-PCR results confirmed the expression of the identified gene signatures in BC. Conclusion: The ML approach helped construct novel diagnostic and prognostic models based on the expression profiling of BC. The identified nine-gene signature and eight-gene signatures showed excellent potential in BC diagnosis and prognosis, respectively.
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Qin, Yuchao, Ahmed Alaa, Andres Floto, and Mihaela van der Schaar. "External validity of machine learning-based prognostic scores for cystic fibrosis: A retrospective study using the UK and Canadian registries." PLOS Digital Health 2, no. 1 (2023): e0000179. http://dx.doi.org/10.1371/journal.pdig.0000179.
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Precise and timely referral for lung transplantation is critical for the survival of cystic fibrosis patients with terminal illness. While machine learning (ML) models have been shown to achieve significant improvement in prognostic accuracy over current referral guidelines, the external validity of these models and their resulting referral policies has not been fully investigated. Here, we studied the external validity of machine learning-based prognostic models using annual follow-up data from the UK and Canadian Cystic Fibrosis Registries. Using a state-of-the-art automated ML framework, we derived a model for predicting poor clinical outcomes in patients enrolled in the UK registry, and conducted external validation of the derived model using the Canadian Cystic Fibrosis Registry. In particular, we studied the effect of (1) natural variations in patient characteristics across populations and (2) differences in clinical practice on the external validity of ML-based prognostic scores. Overall, decrease in prognostic accuracy on the external validation set (AUCROC: 0.88, 95% CI 0.88-0.88) was observed compared to the internal validation accuracy (AUCROC: 0.91, 95% CI 0.90-0.92). Based on our ML model, analysis on feature contributions and risk strata revealed that, while external validation of ML models exhibited high precision on average, both factors (1) and (2) can undermine the external validity of ML models in patient subgroups with moderate risk for poor outcomes. A significant boost in prognostic power (F1 score) from 0.33 (95% CI 0.31-0.35) to 0.45 (95% CI 0.45-0.45) was observed in external validation when variations in these subgroups were accounted in our model. Our study highlighted the significance of external validation of ML models for cystic fibrosis prognostication. The uncovered insights on key risk factors and patient subgroups can be used to guide the cross-population adaptation of ML-based models and inspire new research on applying transfer learning methods for fine-tuning ML models to cope with regional variations in clinical care.
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Hill, Holly A., Preetesh Jain, Michael L. Wang, and Ken Chen. "Abstract 5377: An integrative prognostic machine learning model in mantle cell lymphoma." Cancer Research 83, no. 7_Supplement (2023): 5377. http://dx.doi.org/10.1158/1538-7445.am2023-5377.
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Abstract Background: Mantle cell lymphoma (MCL) is an uncommon B-cell lymphoma. The clinical course is highly variable: some patients have aggressive disease and relapse after treatment, while others have indolent disease or respond exceptionally to frontline therapy. Prognostication of MCL patients is dynamic and continues to evolve as novel therapies develop. Current prognostic indicators, such as the MCL international prognostic index (MIPI), were primarily designed with patients treated with chemo-immunotherapies. Using machine learning (ML) and molecular data, we provide a novel predictive method to improve upon conventional clinical markers. Methods: We studied 785 MCL patients diagnosed at MD Anderson since 2014 and retrospectively classified them as “aggressive MCL” (n=311): relapsed or refractory to frontline treatment, and “mild MCL” (n=474): those who did not relapse after the first treatment (exceptional response) or had indolent disease never requiring treatment. After data extraction and feature engineering, 195 baseline features comprised of clinical, genomic, pathology, and cytogenetic data were integrated into an extreme gradient-boosted ensemble ML model (XGBoost). The dataset containing all patients was split (75/25) into a training and test set. Hyperparameters for the model were tuned using a grid-based, space-filling (Latin hypercube) technique and resampled 10-fold cross-validation from the training set. Training, validation, and testing sets were split using stratification of the classification variable to avoid class imbalance. Results: Our integrative model achieved area under the curve (AUC) = .82 and accuracy = .76 on the test set and outperformed an XGBoost model using only clinical features (AUC = .78, accuracy = .68). Additionally, the fully integrated model improved on metrics from a similar multivariate logistic model including all patients (AUC = .72, accuracy =.72). Univariate logistic models were fit on the classification variable using the MIPI and other prognostic indices. The integrated ML model significantly outperformed the MIPI (AUC = .62, accuracy = .60) and other indices in predicting patient class. Clinical, pathological, cytogenetic, and genomic data were all represented as impactful features in a variable importance plot (VIP) and Shapley (SHAP) additive values of the fully integrated ML model. This model was used to launch a rest application programming interface (API) in which important features could be entered and a prediction returned. Conclusion: Our study demonstrates that the current paradigm of using limited features in disease prognostics should be replaced with more advanced ML models that utilize genomic and other molecular data. Future work will include expanding the features included in the model and using the rest API to construct a graphical user interface accessible to clinicians and other researchers to make treatment decisions in precision oncology. Citation Format: Holly A. Hill, Preetesh Jain, Michael L. Wang, Ken Chen. An integrative prognostic machine learning model in mantle cell lymphoma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5377.
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Filipow, Nicole, Eleanor Main, Neil J. Sebire, et al. "Implementation of prognostic machine learning algorithms in paediatric chronic respiratory conditions: a scoping review." BMJ Open Respiratory Research 9, no. 1 (2022): e001165. http://dx.doi.org/10.1136/bmjresp-2021-001165.
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Machine learning (ML) holds great potential for predicting clinical outcomes in heterogeneous chronic respiratory diseases (CRD) affecting children, where timely individualised treatments offer opportunities for health optimisation. This paper identifies rate-limiting steps in ML prediction model development that impair clinical translation and discusses regulatory, clinical and ethical considerations for ML implementation. A scoping review of ML prediction models in paediatric CRDs was undertaken using the PRISMA extension scoping review guidelines. From 1209 results, 25 articles published between 2013 and 2021 were evaluated for features of a good clinical prediction model using the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines.Most of the studies were in asthma (80%), with few in cystic fibrosis (12%), bronchiolitis (4%) and childhood wheeze (4%). There were inconsistencies in model reporting and studies were limited by a lack of validation, and absence of equations or code for replication. Clinician involvement during ML model development is essential and diversity, equity and inclusion should be assessed at each step of the ML pipeline to ensure algorithms do not promote or amplify health disparities among marginalised groups. As ML prediction studies become more frequent, it is important that models are rigorously developed using published guidelines and take account of regulatory frameworks which depend on model complexity, patient safety, accountability and liability.
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Park, Hyung Soon, Ji Soo Park, Yun Ho Roh, Jieun Moon, Dong Sup Yoon, and Hei-Cheul Jeung. "Prognostic factors and scoring model for survival in advanced biliary tract cancer." Journal of Clinical Oncology 35, no. 4_suppl (2017): 264. http://dx.doi.org/10.1200/jco.2017.35.4_suppl.264.
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264 Background: Metastatic biliary tract cancer (BTC) has dismal prognosis. We herein presented multivariate analysis using routinely evaluated clinico-laboratory parameters at the time of initial diagnosis, to implement a scoring model that can effectively identify risk groups, and we finally validated the model using independent dataset. Methods: From September 2006 to February 2015, 482 patients with metastatic BTC were analyzed. Patients were randomly assigned (7:3) into investigational (n = 340) and validation dataset (n = 142). Continuous variables were dichotomized according to the normal range or the best cutoff values statistically determined by Contal and O’Quigley method. Multivariate analysis using Cox’s proportional hazard model was done to find independent prognostic factors, and scoring model were derived by summing the rounded χ2 scores for the factors emerged in the multivariate analysis. Results: Performance status (ECOG 3-4), hypoalbuminemia ( < 3.4 mg/dL), carcinoembryonic antigen (≥9 ng/mL), neutrophil-lymphocyte ratio (≥3.0), and carbohydrate antigen 19-9 (≥120 U/mL) were identified as independent factors for poor survival in investigational dataset. When assigning patients into three risk groups based on these factors, survival was 14.0, 7.3, and 2.3 months for the low, intermediate, and high-risk groups, respectively (P < 0.001). Harrell’s C-index and integrated AUC for scoring model were 0.682 and 0.653, respectively. In validation dataset, prognosis was also well-divided according to the risk groups (median OS, 16.7, 7.5 and 1.9 months, respectively, P < 0.001). Chemotherapy gave a survival benefit in low and intermediate-risk group (11.4 vs. 4.8 months; P< 0.001), but not in high-risk group (median OS, 4.3 vs. 1.1 months; P = 0.105). Conclusions: We propose a set of prognostic criteria for metastatic BTC, which can help accurate patient risk stratification and aid in treatment selection.
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SUKHOPAROVA, E. P., I. E. KHRUSTALYOVA, E. V. ZINOVIEV, and E. S. KNYAZEVA. "A MODEL FOR ASSESSING THE RISK OF A DELAYED WOUND HEALING IN OBESE PATIENTS." AVICENNA BULLETIN 25, no. 1 (2023): 36–45. http://dx.doi.org/10.25005/2074-0581-2023-25-1-36-46.
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Objective: Develop a model for predicting the risk of a delayed and complicated course of wound healing in obese patients Methods: The study included 49 patients above 30 years of age (mean age 46.98±7.10 years) with a body mass index (BMI) above 25 kg/m2 (mean value 31.64±5.04 kg/m2 ), who underwent augmentation mammaplasty and aesthetic anterior abdominal wall reconstruction in the period from 2016 to 2018. In the postoperative period, the patients were divided into three groups depending on the wound healing pattern: Group I – complicated wound healing (n=21; 42.86%); Group II – delayed wound healing (n=16; 32.65%); Group III – standard wound healing (n=12; 24.49%). The assessment of the prognostic risk of developing a delayed and complicated wound healing was carried out using a new mathematical model, taking into account the insulin level (mIU/l) and spontaneous secretion of interleukin-1β (pg/ml). Logistic regression analysis was used to determine the significance of prognostic factors. The decision tree model was used to stratify risk groups. A receiver operating characteristic (ROC) analysis was used to assess the quality of the constructed model. Results: Using the decision tree, three risk classes of delayed and complicated wound healing were identified. The highest risk of developing postoperative wound complications (risk=95.0%, n=20) was observed in patients with insulin levels ≥14.0 mIU/l. The average level of risk was determined at the value of insulin <14.0 mIU/l and spontaneous production of interleukin-1β ≥51.0 pg/ml (risk=50.0%, n=2). The lowest risk level of complications (risk=3.7%, n=27) was found with a combination of factors: insulin <14.0 mIU/l and interleukin-1β spontaneous production <51.0 pg/ml. The predictive quality of the constructed model is high (the area under the ROC curve is 0.98). Conclusion: The proposed prognostic model will allow the identification of patients with a high risk of delayed or complicated wound healing in the preoperative period and timely adjust the treatment tactics. Keywords: Overweight, wound healing, prognosis, non-healing wounds.
Tesis sobre el tema "ML prognostic model"
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Navicelli, Andrea, Mario Tucci, and Filippo De Carlo. "Analisi ed applicazione di modelli diagnostici e prognostici per guasti e prestazioni di componenti di impianti industriali nell’era I4.0." Doctoral thesis, 2021. http://hdl.handle.net/2158/1234822.
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Il ruolo fondamentale che la manutenzione gioca nei costi di esercizio e nella produttività degli impianti industriali ha portato le aziende e i ricercatori a spostare il loro interesse su questo tema. L'ultima frontiera dell'innovazione in campo manutentivo, resa possibile anche dall'avvento della quarta rivoluzione industriale che promuove la sensorizzazione e l’interconnessione di tutti i macchinari di impianto, è la manutenzione predittiva. Essa mira ad ottenere una previsione accurata della vita utile dei componenti degli impianti industriali al fine di ottimizzare la schedulazione degli interventi sul campo.
Lo studio parte da una accurata revisione della letteratura scientifica di settore riguardante le tecniche diagnostiche e prognostiche applicate a componenti di impianti industriali, necessaria alla comprensione dei diversi modelli sviluppati in funzione della tipologia di componente e modo di guasto in analisi. Successivamente ho spostato l’attenzione sul concetto di manutenzione 4.0 al fine di mappare tutte le caratteristiche associate al paradigma dell'Industria 4.0 e le loro possibili applicazioni alla manutenzione.
Lo studio condotto ha portato poi alla progettazione, sviluppo e validazione delle metodologie necessarie all’applicazione in real-time di modelli diagnostici e prognostici avanzati, sia statistici che machine learning, necessari all’implementazione sul campo di un sistema di manutenzione predittiva.
Grazie all’applicazione delle metodologie proposte ad un caso studio è stato possibile non solo validare i modelli proposti ma anche definire l’architettura informatica necessaria alla loro corretta implementazione sul sistema distribuito di controllo (Distributed Control System - DCS) di impianto in funzione della tipologia del componente e del guasto in analisi.
I modelli testati e validati hanno mostrato elevate prestazioni diagnostiche soprattutto per quanto riguarda i modelli ML che sfruttano le Support Vector Machine (SVM).
In definitiva, questo lavoro di tesi mostra nel dettaglio tutti i passaggi necessari allo sviluppo di un sistema di manutenzione predittiva efficace in impianto: partendo dall’analisi dei modi di guasto e dalla sensorizzazione dei componenti, passando poi allo sviluppo dei modelli diagnostici e prognostici real-time fino alla costruzione dell’interfaccia di visualizzazione dei risultati delle analisi svolte, analizzando anche l’architettura informatica necessaria al suo corretto funzionamento.
The fundamental role that maintenance plays in the operating costs and productivity of industrial plants has led companies and researchers to shift their interest in this issue. The last frontier of innovation in the maintenance field, made possible also by the advent of the fourth industrial revolution which promotes the sensorisation and interconnection of all plant machinery, is predictive maintenance. It aims to obtain an accurate forecast of the useful life of the industrial plants’ components in order to optimise the scheduling of interventions in the field.
The study starts from an accurate review of the scientific literature concerning the diagnostic and prognostic techniques applied to industrial plant components, necessary to understand the different models developed according to the type of component and failure mode under analysis. Subsequently I shifted the focus to the maintenance 4.0 concept in order to map all the characteristics associated with the Industry 4.0 paradigm and their possible applications to maintenance operations.
The study then led to the design, development and validation of the methodologies necessary for the real-time application of advanced diagnostic and prognostic models, both statistical and machine learning, necessary for the field implementation of a predictive maintenance system.
Thanks to the application of the proposed methodologies to a case study, it was possible not only to validate the proposed models but also to define the IT architecture necessary for their correct implementation on the plant's Distributed Control System (DCS) according to the type of component and the fault under analysis.
The tested and validated models showed high diagnostic performance, especially regarding the Support Vector Machine (SVM) Machine Learning models.
Ultimately, this thesis shows in detail all the steps necessary for the development of an effective predictive maintenance system in the plant: starting from the analysis of failure modes and component sensorisation, then moving on to the development of real-time diagnostic and prognostic models up to the build-up of the interface for visualising the results of the analyses carried out, also analysing the IT architecture necessary for its correct operation.
Capítulos de libros sobre el tema "ML prognostic model"
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Aria, Massimo, Corrado Cuccurullo, and Agostino Gnasso. "Supporting decision-makers in healthcare domain. A comparative study of two interpretative proposals for Random Forests." In Proceedings e report. Firenze University Press, 2021. http://dx.doi.org/10.36253/978-88-5518-461-8.34.
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The growing success of Machine Learning (ML) is making significant improvements to predictive models, facilitating their integration in various application fields, especially the healthcare context. However, it still has limitations and drawbacks, such as the lack of interpretability which does not allow users to understand how certain decisions are made. This drawback is identified with the term "Black-Box", as well as models that do not allow to interpret the internal work of certain ML techniques, thus discouraging their use. In a highly regulated and risk-averse context such as healthcare, although "trust" is not synonymous with decision and adoption, trusting an ML model is essential for its adoption. Many clinicians and health researchers feel uncomfortable with black box ML models, even if they achieve high degrees of diagnostic or prognostic accuracy. Therefore more and more research is being conducted on the functioning of these models. Our study focuses on the Random Forest (RF) model. It is one of the most performing and used methodologies in the context of ML approaches, in all fields of research from hard sciences to humanities. In the health context and in the evaluation of health policies, their use is limited by the impossibility of obtaining an interpretation of the causal links between predictors and response. This explains why we need to develop new techniques, tools, and approaches for reconstructing the causal relationships and interactions between predictors and response used in a RF model. Our research aims to perform a machine learning experiment on several medical datasets through a comparison between two methodologies, which are inTrees and NodeHarvest. They are the main approaches in the rules extraction framework. The contribution of our study is to identify, among the approaches to rule extraction, the best proposal for suggesting the appropriate choice to decision-makers in the health domain.
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Yin Bincan, Xin Shichao, and Zhao Yuhong. "Development of Asian Non-Small Cell Lung Cancer Survival Prediction Model Using an Innovative Method of Bayesian Network." In Studies in Health Technology and Informatics. IOS Press, 2017. https://doi.org/10.3233/978-1-61499-830-3-1291.
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We constructed a novel prognostic model using an innovative method of Bayesian Network (BN) to predict Non-Small Cell Lung Cancer survival status within 5 years after operation in the Asian population. The proposed BN model could present the relationship between prognostic factors and showed the highest performance among other machine learning (ML) algorithms.
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Sakagianni, Aikaterini, Christina Koufopoulou, Dimitrios Kalles, Evangelos Loupelis, Vassilios S. Verykios, and Georgios Feretzakis. "Automated ML Techniques for Predicting COVID-19 Mortality in the ICU." In Studies in Health Technology and Informatics. IOS Press, 2023. http://dx.doi.org/10.3233/shti230547.
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The COVID-19 infection is still a serious threat to public health and healthcare systems. Numerous practical machine learning applications have been investigated in this context to support clinical decision-making, forecast disease severity and admission to the intensive care unit, as well as to predict the demand for hospital beds, equipment, and staff in the future. We retrospectively analyzed demographics, and routine blood biomarkers from consecutive Covid-19 patients admitted to the intensive care unit (ICU) of a public tertiary hospital, during a 17-month period, relative to the outcome, in order to build a prognostic model. We used the Google Vertex AI platform, on the one hand, to evaluate its performance in predicting ICU mortality, and on the other hand to show the ease with which even non-experts can make prognostic models. The model’s performance regarding the area under the receiver operating characteristic curve (AUC-ROC) was 0.955. The six highest-ranked predictors of mortality in the prognostic model were age, serum urea, platelets, C-reactive protein, hemoglobin, and SGOT.
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Chakroun, Ayoub, and Nidhal Rezg. "Application of Machine Learning for Predictive and Prognostic Reliability in Flexible Shop floor." In Advances in Logistics Engineering [Working Title]. IntechOpen, 2024. http://dx.doi.org/10.5772/intechopen.1004999.
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Flexible workshops are essential components of modern industry, enabling flexible and efficient production. However, to ensure their proper functioning and prevent unexpected breakdowns, it is crucial to monitor their reliability. Production stoppages caused by unforeseen breakdowns can lead to significant financial losses. This chapter proposes to explore the use of Machine Learning (ML) for predicting the reliability of flexible workshops, thus identifying dates for Preventive Maintenance (PM) interventions and optimizing production management. The objectives of this exploration include the presentation of new predictive model developments and the description of ML models capable of predicting workshop reliability based on real-time data, such as equipment monitoring, production data, and maintenance histories. It also aims to identify optimal times for PM interventions, minimizing production disruptions and optimizing resource utilization. Additionally, the chapter will propose cost optimization models to prevent unplanned breakdowns, extend equipment lifespan, optimize spare parts usage, and maximize productivity by avoiding production interruptions and ensuring the smooth operation of the flexible workshop.
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Mouazer, Abdelmalek, Edgar Degroodt, Florence Nguyen-Khac, and Elise Chapiro. "Investigating AI Approaches for Survival Prediction in Chronic Lymphocytic Leukemia." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250056.
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Chronic lymphocytic leukemia (CLL) exhibits a heterogeneous clinical course. Prognostic markers that impact patient outcomes have been identified, including MYC gene abnormalities. This study investigates machine learning (ML) models for predicting survival in CLL, comparing the performance of Random Survival Forest (RSF), Decision Tree (DT), and Cox proportional hazards models across two cohorts: MYC-positive patients and a general CLL population. Three time-to-event outcomes were assessed: 10-year from diagnosis, 10-year from cytogenetic assessment, and time to first treatment. Model performance was evaluated using the C-index and AUC, revealing that RSF and DT models outperformed Cox models in predictive accuracy. Permutation importance highlighted key predictive variables; however, RSF and DT models pose interpretability challenges compared to Cox models, which offer clear hazard ratios. Additionally, an interactive application is available via Streamlit, and the source code is open access on GitHub. Despite limitations in dataset size and external validity, ML models show promise for personalized survival predictions in CLL, especially for MYC-positive cases, underscoring the potential for further model refinement to enhance clinical usability.
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Varol, Buğra. "TRIPOD+AI ve TRIPOD-LLM Rehberlerine Uyum: Klinik Yapay Zeka Modelleri Nasıl Raporlanmalı?" In Sağlık Bilimlerinde Bütüncül Perspektifler ve Klinik Süreçler. Özgür Yayınları, 2025. https://doi.org/10.58830/ozgur.pub780.c3256.
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Bu bölüm, klinik yapay zeka (Artificial Intelligence, AI) ve makine öğrenimi (Machine Learning, ML) temelli tahmin modellerinin şeffaf raporlanmasına yönelik Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Artificial Intelligence (TRIPOD+AI, 2024) ile Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis–Large Language Models (TRIPOD-LLM, 2025) kılavuzlarını sistematik biçimde değerlendirmektedir. Öncelikle TRIPOD’ın klasik sürümünden bu yana metodolojik gereksinimlerin nasıl evrildiğini ortaya koymaktadır. İkinci kısımda, TRIPOD+AI’nin on dört ek maddesi ve TRIPOD-LLM’nin büyük dil modelleri özel ilkeleri üzerinden veri yönetimi, model geliştirme-doğrulama, yorumlanabilirlik, etik ve yanlılık analizleri için ayrıntılı kontrol listeleri sunmaktadır. Eksik veri yönetimi, veri sızıntısı, tekil metrik kullanımı ve haricî doğrulama eksikliği gibi yaygın hataları örnekleyerek bunlara yönelik önleme stratejilerini tartışmaktadır. Son bölüm, biyoistatistikçilerin kuramsal rehberlik rolünü vurgulamakta; federatif öğrenme, gizlilik-korumalı AI ve adillik-odaklı değerlendirmeler ışığında geleceğe yönelik araştırma gündemini çizmektedir.
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Uludag, Kadir. "Hyperparameters and Tuning Methods for Random Forest Using Python Sklearn Package Relevant to Psychology Studies." In Advances in Medical Technologies and Clinical Practice. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-2703-6.ch011.
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Machine learning (ML) is used to create well-functioning prediction models for predicting the prognosis of psychiatric disease or to distinguish the disease from other psychiatric diseases such as distinguishing schizophrenia from methamphetamine addiction. Parameter tuning is necessary to create optimum machine learning (ML) models that successfully produce solutions for classification or regression problems. ML methods such as random forest (RF) and support vector machine (SVM) are commonly used in prediction studies in both psychology and psychiatry literature for solving various complex problems. However, studies are not consistent in terms of ML methods since they may adopt different hyperparameter tuning strategies, or they may not report their use of the ML method. For example, some researchers may use autotuning ML methods while others may prefer designing the code by themselves without using default values of automatically designed ML methods. Thereby, it is important to identify and explain the methodologic aspects of the ML method to have a reproducible output.
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Rohith R, Sakthi Jaya Sundar Rajasekar, Thangavel Murugan, and Varalakshmi Perumal. "Enhanced Handwriting Kinematic Modeling for Alzheimer’s Disease Classification Using Machine Learning Models." In Studies in Health Technology and Informatics. IOS Press, 2025. https://doi.org/10.3233/shti250684.
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Alzheimer’s Disease (AD) is a neurodegenerative disorder that gradually deteriorates motor and cognitive abilities, including handwriting abilities. This study explores the effectiveness of handwriting analysis in detecting AD by leveraging Machine Learning (ML) techniques. A dataset containing handwriting samples was preprocessed using normalization and Synthetic Minority Over-Sampling Technique (SMOTE) to balance class distribution. Multiple ML models were trained and evaluated. Among the tested models, the highest classification accuracy, 99.26%, was attained by Multi-Layer Perceptron (MLP). The findings suggest that handwriting-based assessment, combined with advanced ML techniques, can serve as a promising non-intrusive tool for screening and evaluating the prognosis of AD.
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Varshini, Vemasani, Maheswari Raja, and Sharath Kumar Jagannathan. "Endometrial Cancer Detection Using Pipeline Biopsies Through Machine Learning Techniques." In Advances in Systems Analysis, Software Engineering, and High Performance Computing. IGI Global, 2024. http://dx.doi.org/10.4018/979-8-3693-1131-8.ch007.
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Endometrial carcinoma (EC) is a common uterine cancer that leads to morbidity and death linked to cancer. Advanced EC diagnosis exhibits a subpar treatment response and requires a lot of time and money. Data scientists and oncologists focused on computational biology due to its explosive expansion and computer-aided cancer surveillance systems. Machine learning offers prospects for drug discovery, early cancer diagnosis, and efficient treatment. It may be pertinent to use ML techniques in EC diagnosis, treatments, and prognosis. Analysis of ML utility in EC may spur research in EC and help oncologists, molecular biologists, biomedical engineers, and bioinformaticians advance collaborative research in EC. It also leads to customised treatment and the growing trend of using ML approaches in cancer prediction and monitoring. An overview of EC, its risk factors, and diagnostic techniques are covered in this study. It concludes a thorough investigation of the prospective ML modalities for patient screening, diagnosis, prognosis, and the deep learning models, which gave the good accuracy.
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A, Dr Mariyan Richard, Ms Joy Lavinya, and Dr Prasad Naik Hamsavath. "DESIGN AN ML MODEL FOR PREDICTING HEART DISEASE AND INTEGRATE THE MODEL." In Futuristic Trends in Artificial Intelligence Volume 3 Book 8. Iterative International Publisher, Selfypage Developers Pvt Ltd, 2024. http://dx.doi.org/10.58532/v3bgai8p4ch6.
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The prognosis of heart disease is one of the most challenging problems in contemporary medicine. Nearly one person dies from heart disease every minute in the modern world. To process vast amounts of data, the healthcare sector needs data science. The study's findings show how well the model predicts heart disease and outperforms other methods while providing information on the most important risk variables. In general, this study advances the rapidly expanding field of machine learning (ML) applications in healthcare and highlights the significance of responsible model creation and use for improved patient care and outcomes. Due to the difficulty of forecasting cardiac illness, automation of the technique is essential to reduce risks and provide the patient with early warning. A properly split dataset is used to train the chosen ML model, and hyper parameter adjustment is done to enhance performance. The model's prediction accuracy is evaluated using metrics including accuracy, precision, recall, F1 score, and area under the curve. The model is used in the real world once it performs satisfactorily.
Actas de conferencias sobre el tema "ML prognostic model"
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Almeida Filho, Benedito de Sousa, Michelle Sako Omodei, Eduardo Carvalho Pessoa, Heloisa de Luca Vespoli, and Eliana Aguiar Petri Nahas. "NEGATIVE IMPACT OF SERUM VITAMIN D DEFICIENCY ON BREAST CANCER SURVIVAL." In XXIV Congresso Brasileiro de Mastologia. Mastology, 2022. http://dx.doi.org/10.29289/259453942022v32s1058.
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Introduction: It is known that breast cancer is the type of cancer that mostly affects women in the world, both in the developing and developed countries, with about 2.3 million new cases in 2020, comprising 25% of all cancers diagnosed in women. Vitamin D concentration has been studied as a risk and prognostic factor in women with breast cancer; its deficiency is common in women with postmenopausal breast cancer, and some evidence suggests that low vitamin D status increases the risk for disease development. The impact of vitamin D at the time of diagnosis on the outcome of patients with breast cancer is less well understood. In view of the increasing number of breast cancer survivors and the high prevalence of vitamin D deficiency among patients with breast cancer, an evaluation of the role of vitamin D in prognosis and survival among patients with breast cancer is essential. Objective: The aim of this study was to evaluate the association between serum vitamin D (VD) levels at diagnosis and overall survival (OS), disease-free survival (DFS), and cancer-specific survival (CSS) in postmenopausal women treated for breast cancer. Methods: This is a single-center prospective cohort. The study included patients newly diagnosed with invasive breast cancer between 2014 and 2016, aged ≥45 years, and in amenorrhea for ≥12 months, and VD assessment at the time of diagnosis, before any cancer treatment. Patients were classified into three groups according to serum levels of 25-hydroxyvitamin-D [25(OH)D]: sufficient (≥30 ng/mL), insufficient (between 20 and 29 ng/mL), and deficient (<20 ng/mL). Clinical and anatomopathological data were collected. The primary outcome was OS and secondary outcomes were DFS and CSS. Kaplan-Meier curve and Cox regression model were used to assess the association between 25(OH)D levels and OS, DFS, and CSS. Differences in survival were evaluated by hazard ratios (HRs). The study was approved by the Ethics Committee (CAAE: 71399117.2.0000.5411). Results: The study included 192 women with a mean age of 61.3±9.6 years at diagnosis, mean 25(OH)D levels of 25.8 ng/mL (ranging from 12.0 to 59.2 ng/mL), and follow-up period between 54 and 78 months. Sufficient VD levels were detected in 65 patients (33.9%), insufficient in 92 (47.9%), and deficient in 35 (18.2%). Patients with 25(OH)D insufficiency and deficiency had a larger proportion of high-grade tumors, locally advanced and with distant metastasis, positive axillary lymph nodes, negative estrogen receptors (ER), and progesterone receptors (PR), and higher Ki67 index (p<0.05 ). The mean OS time was 54.4±20.2 months (range 9–78 months), and 51 patients (26.6%) died during the study period. Patients with VD deficiency and insufficiency at diagnosis had significantly lower OS, DFS, and CSS compared to patients with sufficient values (p <0.0001). After the adjustment for clinical and tumoral prognostic factors, patients with serum 25(OH)D levels considered deficient at the time of diagnosis had a significantly higher risk of global death (HR=4.65, 95%CI 1.65–13.12), higher risk of disease recurrence (HR=6.87, 95%CI 2.35–21.18), and higher risk of death from the disease (HR=5.91, 95%CI 1.98–17.60) than the group with sufficient 25(OH)D levels.
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Rodrigues, Diego Dimer, and Mariana Recamonde-Mendoza. "Bias Propagation in Health AI: Measuring Pre-Training Bias and Its Effect on Machine Learning Model Outcomes." In Simpósio Brasileiro de Computação Aplicada à Saúde. Sociedade Brasileira de Computação - SBC, 2025. https://doi.org/10.5753/sbcas.2025.7143.
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Machine learning (ML) has become an essential tool in healthcare, supporting diagnosis, prognosis, and treatment decisions. However, biases present in pre-training data can compromise both model performance and fairness, disproportionately affecting underrepresented groups. This study systematically examines the impact of four pre-training bias metrics on the accuracy of three ML models across four health-related datasets. Our findings show that more data does not necessarily translate to better performance, particularly when data imbalance and bias are present. Moreover, pre-training bias metrics are associated with accuracy disparities, underscoring the importance of proactive bias assessment to develop more equitable ML models in healthcare.
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Duman, A., J. Powell, S. Thomas, and E. Spezi. "Evaluation of Radiomic Analysis over the Comparison of Machine Learning Approach and Radiomic Risk Score on Glioblastoma." In Cardiff University Engineering Research Conference 2023. Cardiff University Press, 2024. http://dx.doi.org/10.18573/conf1.f.
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Accurate patient prognosis is important to provide an effective treatment plan for Glioblastoma (GBM) patients. Radiomics analysis extracts quantitative features from medical images. Such features can be used to build models to support medical decisions for diagnosis, prognosis, and therapeutic response. The progress of radiomics analysis is continuously improving. The aim of this research is to extract standardised radiomic features from MRI scans of GBM patients, perform feature selection, and compare radiomic-based risk score (RRS) and machine learning (ML) approaches for the risk stratification of GBM patients. We have also tested the generalisability of these models which is crucial for clinical implementation. Our work demonstrates that a stratification model based on logistic regression generalised better than the RRS method when applied to new unseen datasets.
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Laghmati, Sara, Khadija Hicham, Soufiane Hamida, Karima Boutahar, Bouchaib Cherradi, and Amal Tmiri. "A CAD System Based On a Stacked Ensemble Model and ML Techniques for Breast Cancer Prognosis." In 2023 3rd International Conference on Innovative Research in Applied Science, Engineering and Technology (IRASET). IEEE, 2023. http://dx.doi.org/10.1109/iraset57153.2023.10152913.
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Belov, D., A. Kolyshkin, B. Reid, et al. "The Digitization of Mud Motor Power Section Life Cycle: From Concept to Operation." In ADIPEC. SPE, 2023. http://dx.doi.org/10.2118/216138-ms.
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Abstract In this paper, we introduce a fresh perspective on the life cycle of mud motor power sections. Rather than following the conventional steps associated with this well-established mechanical tool, we have reevaluated and reimagined the entire process. Our innovative approach leverages the digital to facilitate the development, optimization, and maintenance of power sections. By implementing this approach, we can augment the value and functionality of power sections without any costly redesigns. Our focus is on three essential elements of the power section life cycle: designing the power section to meet field requirements, selecting the ideal power section for the job based on drilling conditions, and monitoring the health of the power section during operation. To achieve these goals, we integrated a state-of-the-art physical model of the power section with the power of machine learning (ML) and data science. With this model, we can simulate the power section's performance and durability during the design and optimization stages and monitor its fatigue life in real time using a digital twin approach. The utilization of digital capabilities enables adoption of a systematic approach toward the mud motor power section life cycle. This utilization resulted in increased drilling performance, reduced nonproduction time, and a significant decrease in field failures. Digitalizing the development of the power sections reduced time-to-market and produced customized products by addressing field requests through modeling. It also helps identify the optimal downhole environment for maximum model performance. To optimize the power section for drilling, a modeling method was used to select the best model and define drilling parameters based on drilling requirements and equipment. This approach helps to significantly improve rate of penetration (ROP) while reducing power section damage. Defining power sections with high performance and durability alongside optimal drilling parameters enhances drilling efficiency. Our maintenance process incorporates an industry-unique prognostic and health management method. This approach enables real-time tracking of the power section's remaining useful life, minimizing the likelihood of failure. By accurately determining each power section's remaining resources, we can decide on its further utilization or retirement, leading to the fleet optimization. In summary, our complex solution based on a digital approach offers a dependable and effective tool for achieving top-notch life cycle management for power sections. The proposed approach is a first-of-its-kind solution that combines all the critical stages of the mud motor power section life cycle. This innovative approach showcases the significant value of digital technology in providing additional functionality and creating new services for traditional mechanical power sections. The proposed solution offers a comprehensive and unique solution that significantly enhances the cumulative commercial impact on all three stages of the power section life cycle.
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Al-Mannai, Rashid Ebrahim, Mohammed Hamad Almerekhi, Mohammed Abdulla Al-Mannai, et al. "Artificial Intelligence in Predicting Heart Failure." In Qatar University Annual Research Forum & Exhibition. Qatar University Press, 2021. http://dx.doi.org/10.29117/quarfe.2021.0130.
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Heart Failure is a major chronic disease that is increasing day by day and a great health burden in health care systems world wide. Artificial intelligence (AI) techniques such as machine learning (ML), deep learning (DL), and cognitive computer can play a critical role in the early detection and diagnosis of Heart Failure Detection, as well as outcome prediction and prognosis evaluation. The availability of large datasets from difference sources can be leveraged to build machine learning models that can empower clinicians by providing early warnings and insightful information on the underlying conditions of the patients
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Kornev, Denis, Roozbeh Sadeghian, Stanley Nwoji, Qinghua He, Amir Gandjbbakhche, and Siamak Aram. "Machine Learning-Based Gaming Behavior Prediction Platform." In 13th International Conference on Applied Human Factors and Ergonomics (AHFE 2022). AHFE International, 2022. http://dx.doi.org/10.54941/ahfe1001826.
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Brain disorders caused by Gaming Addiction drastically increased due to the rise of Internet users and Internet Gaming auditory. Driven by such a tendency, in 2018, World Health Organization (WHO) and the American Medical Association (AMA) addressed this problem as a “gaming disorder” and added it to official manuals. Scientific society equipped by statistical analysis methods such as t-test, ANOVA, and neuroimaging techniques, such as functional magnetic resonance imaging (fMRI), positron emission tomography (PET), and electroencephalography (EEG), has achieved significant success in brain mapping, examining dynamics and patterns in different conditions and stages. Nevertheless, more powerful, self-learning intelligent algorithms are suitable not only to evaluate the correlation between gaming addiction patterns but also to predict behavior and prognosis brain response depending on the addiction severity. The current paper aims to enrich the knowledge base of the correlation between gaming activity, decision-making, and brain activation, using Machine Learning (ML) algorithms and advanced neuroimaging techniques. The proposed gaming behavior patterns prediction platform was built inside the experiment environment composed of a Functional Near-Infrared Spectrometer (fNIRS) and the computer version of Iowa Gambling Task (IGT). Thirty healthy participants were hired to perform 100 cards selection while equipped with fNIRS. Thus, accelerated by IGT gaming decision-making process was synchronized with changes of oxy-hemoglobin (HbO) levels in the human brain, averaged, and investigated in the left and the right brain hemispheres as well as different psychosomatic conditions, conditionally divided by blocks with 20 card trials in each: absolute unknown and uncertainty in the first block, “pre-hunch” and “hunch” in the second and third blocks, and conceptuality and risky in the fourth and fifth blocks. The features space was constructed around the HbO signal, split by training and tested in two proportions 70/30 and 80/20, and drove patterns prediction ML-based platform consisted of five mechanics, such as Multiple Regression, Classification and Regression Trees (CART), Support Vector Machine (SVM), Artificial Neural Network (ANN), and Random Forest. The algorithm prediction power was validated by the 5- and 10-fold cross-validation method and compared by Root Mean Squared Error (RMSE) and coefficient of determination (R Squared) metrics. Indicators of “the best” fit model, lowest RMSE, and highest R Squared were determined for each block and both brain hemispheres and used to make a conclusion about prediction accuracy: SVM algorithm with RBF kernel, Random Forest, and ANN demonstrated the best accuracy in most cases. Lastly, “best fit” classifiers were applied to the testing dataset and finalized the experiment. Hence, the distribution of gaming score was predicted by five blocks and both brain hemispheres that reflect the decision-making process patterns during gaming. The investigation showed increasing ML algorithm prediction power from IGT block one to five, reflecting an increasing learning effect as a behavioral pattern. Furthermore, performed inside constructed platform simulation could benefit in diagnosing gaming disorders, their patterns, mechanisms, and abnormalities.
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Viale, Luca, Alessandro Paolo Daga, Luigi Garibaldi, Salvatore Caronia, and Ilaria Ronchi. "Books Trimmer Industrial Machine Knives Diagnosis: A Condition-Based Maintenance Strategy Through Vibration Monitoring via Novelty Detection." In ASME 2022 International Mechanical Engineering Congress and Exposition. American Society of Mechanical Engineers, 2022. http://dx.doi.org/10.1115/imece2022-94547.
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Abstract In recent years, Artificial Intelligence (AI) is ever more exploited in all the scientific and industrial fields and is allowing significant developments in mechanical engineering too. An emblematic contribution was given in terms of safety and reliability since Machine Learning (ML) techniques permitted the monitoring and the prediction of the state of health of machinery, allowing the adoption of predictive maintenance strategies. In fact, data-driven models — based on acquisitions — attract considerable interest both thanks to its theoretical and application development. The evolution of diagnostic techniques is oriented towards Condition-Based Maintenance (CBM) strategies, thus allowing improvements in terms of safety enhancement, cost reduction and increased performances. This paper proposes the development and implementation of a diagnostic/prognostic tool applied to an automated books trimmer industrial machine, implementing condition monitoring by means of accelerometers which can be integrated into a Supervisory Control And Data Acquisition (SCADA) system. Given its use, the core components of this production line are three knives, subjected to significant impulsive forces. Therefore, the target of the work is to infer the wear of these three knives, as they are critical elements of the machinery and have a high impact on the quality of the final product. The project was carried out in collaboration with Tecnau — an industry-leading company — which made it possible to conduct experimentation and data acquisition on their machinery. An appropriate Design Of Experiments (DOE) and the use of inferential statistical techniques — such as the ANalysis Of VAriance (ANOVA) and the identification of significant effects — applied to the multivariate dataset allowed recognizing the most relevant features for Novelty Detection (ND). Both the Linear Discriminant Analysis (LDA) and the k-Nearest Neighbors (kNN) method permitted to correctly distinguish the patterns representing the health conditions of the machinery, classifying the data in the reduced multidimensional space according to the final product quality. The results obtained in terms of accuracy are very positive and promising. This means that the developed method is able to successfully identify the state of health of the blade in spite of varying functioning parameters (book thickness and size, paper type and characteristics) and operating conditions. The algorithm speed and its integration into the industrial line make a real-time condition-based maintenance strategy possible. This diagnostic method is suitable for applications oriented to the paradigm of Industry 4.0 and the digitalization of the industrial sector, which can be integrated with the Internet of Things (IoT) and cloud systems.
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Апарцин, Константин, and Konstantin Apartsin. "The results of fundamental and translational research carried out In the Department of Biomedical Research and Technology of the SBRAS INC in 2012-2016." In Topical issues of translational medicine: a collection of articles dedicated to the 5th anniversary of the day The creation of a department for biomedical research and technology of the Irkutsk Scientific Center Siberian Branch of RAS. INFRA-M Academic Publishing LLC., 2017. http://dx.doi.org/10.12737/conferencearticle_58be81eca22ad.
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The results of basic and translational research of the Department of Biomedical Research and Technology of Irkutsk Scientific Center of the Siberian Branch of the Russian Academy of Sciences in 2012–2016 The paper presents the results of interdisciplinary research carried out in 2012–2016. The review includes the study of molecular mechanisms of pathogenesis of reparative regeneration, experimental substantiation of methods of diagnosis and prognosis of systemic disturbances of regeneration process, carrying out clinical trials of medicinal products and the formation of observational studies in the field of personalized medicine, the preparation of practical recommendations on the testing of previously developed surgical methods of prevention or correction of postoperative recovery disorders. New data are obtained on the role of the MAP-kinase cascade in the process of regeneration of muscle tissue. It has been established, that with a significant increase of VEGF concentration at the site of the repair of ischemic myocardium, progenitor cells with the CD34+CD45+ phenotype appear, which opens up prospects for the development of biotechnology to restore the damaged myocardium with its own pool of progenitor cells. The new data on the role of growth factors in the post-infarction remodeling are found. It has been revealed, that in local increase of selenium concentration low intensity of mineralization of forming callus in the area of the damage is observed and the formation of bone regeneration slows down. Prospects for the use of nanocomposites of elemental selenium for modulation of reparative response are marked. The dynamics of the level of free circulating mitochondrial DNA (mtDNA) of blood in the early stages of experimental dyslipidemia has been studied. Atherogenic blood factors do not have a significant effect on the release of the mtDNA from dyslipidemia target cells. On the model of acute small-focal myocardial ischemia, we revealed the increase in the mtDNA levels. Prospects of broadcast of diagnostic mtDNA monitoring technology in myocardial ischemia have been marked. The mtDNA monitoring was first tested as a molecular risk pattern in acute coronary syndrome. In survived patients, the concentration of freely circulating mtDNA in blood plasma was 164 times lower. The probability of death of the patient with a high level of mtDNA (over 4000 copies/mL) was 50 % (logit analysis). Methodological level of translational research in the ISC SB RAS has increased due to effective participation in international multi-center clinical trials of drugs, mainly direct anticoagulants: fondaparinux, edoksabana, betriksabana. “Feedback broadcast” of the results of clinical trials of p38-kinase inhibitor, was carried out in the process of changing the model (initially – neuropathic pain) for coronary atherosclerosis. Technologies of pharmacogenetic testing and personalized treatment of diseases in the employees of the Irkutsk Scientific Center were applied. Step T2. Previously developed at the Irkutsk State Medical University and the Irkutsk Scientific Center of Surgery and Traumatologies approaches to surgical prevention and medicinal correction of postoperative hyposplenism were translated into practical health care. Thus, these results obtained in different areas of translational medicine will determine scientific topics of the department in future research cycle.