Relevant bibliographies by topics / ML prognostic model

Contents

  1. Journal articles
  2. Dissertations / Theses
  3. Book chapters
  4. Conference papers

Academic literature on the topic 'ML prognostic model'

Author: Grafiati
Published: 10 March 2023
Last updated: 31 July 2025

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Journal articles on the topic "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 ind
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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
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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 varia
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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
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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 constructio
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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
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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 predictiv
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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 be
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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 norm
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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 wou
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Dissertations / Theses on the topic "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
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Book chapters on the topic "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,
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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,
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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
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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 perform
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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 koymaktadı
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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 pr
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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)
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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 onc
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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 im
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Conference papers on the topic "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 brea
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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 met
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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 stratific
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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
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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 underlyin
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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 ma
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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 evolut
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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 i
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