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Journal articles on the topic 'ML prognostic model'

Author: Grafiati

Published: 10 March 2023

Last updated: 31 July 2025

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1

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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Alshwayyat, Sakhr Abdulsalam, Abdalwahab Alenezy, Mustafa Alshwayyat, and Tala Abdulsalam Alshwayyat. "Laryngeal squamous cell carcinoma (LSCC) prognosis and machine learning insights." Journal of Clinical Oncology 42, no. 16_suppl (2024): e18058-e18058. http://dx.doi.org/10.1200/jco.2024.42.16_suppl.e18058.

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e18058 Background: LSCC has seen a rise in cases and deaths over the past 30 years. Considering the evolving therapeutic options available in the treatment landscape, we evaluated treatment approaches and developed a machine learning (ML) model for patients with LSCC without distant metastasis. Methods: Data from 2000 to 2020 were obtained from the National Cancer Institute Surveillance, Epidemiology, and End Results database with localized/regional stages only, including the glottis (GC), supraglottic (SuGC), and subglottic (SGC). Patients who were not diagnosed based on histology, previous h

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Nadali, Gianpaolo, Luisa Tavecchia, Elisabetta Zanolin, et al. "Serum Level of the Soluble Form of the CD30 Molecule Identifies Patients With Hodgkin's Disease at High Risk of Unfavorable Outcome." Blood 91, no. 8 (1998): 3011–16. http://dx.doi.org/10.1182/blood.v91.8.3011.3011_3011_3016.

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Preliminary reports suggested a prognostic significance for serum levels of soluble CD30 (sCD30) in patients with Hodgkin's disease (HD). In this study, we investigated the prognostic impact of sCD30 concentration at diagnosis in relation to the other recognized prognostic parameters in 303 patients with HD observed in three different institutions between 1984 and 1996. sCD30 levels were correlated with stage, presence of B symptoms, and tumor burden. High sCD30 levels entailed a higher risk of poor outcome, and the event-free survival (EFS) probability at 5 years for patients with sCD30 level

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Li, Jiancheng, and Houjun Jia. "Serum IL-17A as a Diagnostic and Prognostic Biomarker in Colorectal Cancer: Development and Validation of a Multi-Indicator Model." Research in Health Science 10, no. 2 (2025): p84. https://doi.org/10.22158/rhs.v10n2p84.

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Objective: Colorectal cancer ranks as the third most prevalent malignancy globally. To address the limited sensitivity of conventional biomarkers, this study evaluated the clinical utility of preoperative serum interleukin-17A (IL-17A) in CRC diagnosis and prognosis. We aimed to develop a multi-indicator diagnostic model and assess its prognostic efficacy.Methods: This study involved 126 patients diagnosed with colorectal cancer (CRC) who underwent radical resection at the First Affiliated Hospital of Chongqing Medical University between June 2022 and December 2023. Additionally, a control gro

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Su, T., H. Wu, L. Wu, M. Zhi, and J. Yao. "P0877 Machine Learning and Mendelian Randomization Analysis for Predicting Endoscopic Restenosis in Patients with Crohn's Disease after Endoscopic Balloon Dilation." Journal of Crohn's and Colitis 19, Supplement_1 (2025): i1671—i1672. https://doi.org/10.1093/ecco-jcc/jjae190.1051.

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Abstract Background Endoscopic balloon dilation (EBD) is a safe and effective procedure for treating stenosis in patients with Crohn's disease (CD). This study aimed to evaluate factors associated with endoscopic restenosis after EBD and construct a prognostic model. Methods We retrospectively collected and analyzed data on patients receiving EBD treatment at the Sixth Affiliated Hospital of Sun Yat-sen University from 2013 to 2024. Seven machine learning (ML) algorithms were used to construct a prognostic model. And explore potential biomarkers of intestinal stricture using Mendelian randomiz

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Bel’skaya, L. V., and V. K. Kosenok. "A new field of application of saliva tests for prognostic purpose: focus on lung cancer." Biomedical Chemistry: Research and Methods 3, no. 3 (2020): e00133. http://dx.doi.org/10.18097/bmcrm00133.

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The aim of this work was to determine the potential prognostic role of the biochemical parameters of saliva in lung cancer. The study included 425 patients with lung cancer of various histological types. Saliva samples were collected before treatment and 34 biochemical parameters were determined. Prognostic factors were analyzed by multivariate analysis using Cox’s proportional hazard model. Results have shown that for squamous cell carcinoma, LDH activity below 1748 U/L was an independent prognostically unfavorable factor (HR = 2.89; 95% CI 1.28 – 6.46; р = 0.00330). For adenocarcinoma, there

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Ferroni, Patrizia, Fabio Zanzotto, Silvia Riondino, Noemi Scarpato, Fiorella Guadagni, and Mario Roselli. "Breast Cancer Prognosis Using a Machine Learning Approach." Cancers 11, no. 3 (2019): 328. http://dx.doi.org/10.3390/cancers11030328.

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Machine learning (ML) has been recently introduced to develop prognostic classification models that can be used to predict outcomes in individual cancer patients. Here, we report the significance of an ML-based decision support system (DSS), combined with random optimization (RO), to extract prognostic information from routinely collected demographic, clinical and biochemical data of breast cancer (BC) patients. A DSS model was developed in a training set (n = 318), whose performance analysis in the testing set (n = 136) resulted in a C-index for progression-free survival of 0.84, with an accu

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Dzis, Ivan, Oleksandra Tomashevska, Yevhen Dzis, and Zoryana Korytko. "Prediction of survival in non-Hodgkin lymphoma based on markers of systemic inflammation, anemia, hypercoagulability, dyslipidemia, and Eastern Cooperative Oncology Group performance status." Acta Haematologica Polonica 51, no. 1 (2020): 34–41. http://dx.doi.org/10.2478/ahp-2020-0008.

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AbstractBackgroundThe International Prognostic Index and its modifications are used to estimate prognosis in non-Hodgkin lymphoma. However, the outcome is often different in patients with similar index scores.AimThe aim of this study was to elaborate a prognostic model for patients with mature B-cell non-Hodgkin lymphoma using a combination of predictive markers.Material and methodsThe study included 45 patients with mature B-cell non-Hodgkin lymphoma. Before the administration of treatment, clinical and laboratory parameters were measured. After the 35-month follow-up period, overall survival

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Yagin, Fatma Hilal, Ahmadreza Shateri, Hamid Nasiri, Burak Yagin, Cemil Colak, and Abdullah F. Alghannam. "Development of an expert system for the classification of myalgic encephalomyelitis/chronic fatigue syndrome." PeerJ Computer Science 10 (March 20, 2024): e1857. http://dx.doi.org/10.7717/peerj-cs.1857.

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Myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) is a severe condition with an uncertain origin and a dismal prognosis. There is presently no precise diagnostic test for ME/CFS, and the diagnosis is determined primarily by the presence of certain symptoms. The current study presents an explainable artificial intelligence (XAI) integrated machine learning (ML) framework that identifies and classifies potential metabolic biomarkers of ME/CFS. Metabolomic data from blood samples from 19 controls and 32 ME/CFS patients, all female, who were between age and body mass index (BMI) frequenc

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Hulsbergen, Alexander, Yu Tung Lo, Vasileios Kavouridis, et al. "SURG-02. SURVIVAL PREDICTION AFTER NEUROSURGICAL RESECTION OF BRAIN METASTASES: A MACHINE LEARNING APPROACH." Neuro-Oncology 22, Supplement_2 (2020): ii203. http://dx.doi.org/10.1093/neuonc/noaa215.849.

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Abstract INTRODUCTION Survival prediction in brain metastases (BMs) remains challenging. Current prognostic models have been created and validated almost completely with data from patients receiving radiotherapy only, leaving uncertainty about surgical patients. Therefore, the aim of this study was to build and validate a model predicting 6-month survival after BM resection using different machine learning (ML) algorithms. METHODS An institutional database of 1062 patients who underwent resection for BM was split into a 80:20 training and testing set. Seven different ML algorithms were trained

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Handayani, Lilies, Denis Chegodaev, Ray Steven, and Kenji Satou. "Identification of Key Genes Associated with Overall Survival in Glioblastoma Multiforme Using TCGA RNA-Seq Expression Data." Genes 16, no. 7 (2025): 755. https://doi.org/10.3390/genes16070755.

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Background/Objectives: Glioblastoma multiforme (GBM) is an aggressive and heterogeneous brain tumor with poor prognosis, emphasizing the need for reliable molecular biomarkers to improve patient stratification and treatment planning. This study aimed to identify key genes associated with overall survival in GBM by employing and comparing machine learning (ML) and deep learning (DL) approaches using RNA-Seq gene expression data. Methods: RNA-Seq expression and clinical data for primary GBM tumors were obtained from The Cancer Genome Atlas (TCGA). A univariate Cox proportional hazards regression

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Aljawabrah, Salsabeel, Sakhr Alshwayyat, Kholoud Alqasem, et al. "Identifying key prognostic indicators in Wilms tumor using machine learning techniques." Journal of Clinical Oncology 43, no. 16_suppl (2025): 4557. https://doi.org/10.1200/jco.2025.43.16_suppl.4557.

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4557 Background: Wilms tumor is a rare pediatric malignancy, accounting for 6% of pediatric tumors and primarily affecting the kidneys. Its impact on quality of life and long-term outcomes complicates management. This study leveraged machine learning (ML) to identify prognostic factors with the aim of enhancing prognosis and survival rates. Methods: Data were obtained from the SEER database (2004-2021). Patients who met any of the following criteria were excluded: diagnosis not confirmed by histology, previous history of cancer or other concurrent malignancies, or unknown data. To identify pro

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Muscas, Giovanni, Tommaso Matteuzzi, Eleonora Becattini, et al. "Development of machine learning models to prognosticate chronic shunt-dependent hydrocephalus after aneurysmal subarachnoid hemorrhage." Acta Neurochirurgica 162, no. 12 (2020): 3093–105. http://dx.doi.org/10.1007/s00701-020-04484-6.

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Abstract Background Shunt-dependent hydrocephalus significantly complicates subarachnoid hemorrhage (SAH), and reliable prognosis methods have been sought in recent years to reduce morbidity and costs associated with delayed treatment or neglected onset. Machine learning (ML) defines modern data analysis techniques allowing accurate subject-based risk stratifications. We aimed at developing and testing different ML models to predict shunt-dependent hydrocephalus after aneurysmal SAH. Methods We consulted electronic records of patients with aneurysmal SAH treated at our institution between Janu

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Setiawan, Rinaldy T., Eko Prasetyo, Maximillian Ch Oley, and Fredrik G. Langi. "Relationship between Serum Fibronectin and Level of Consciousness according to FOUR Score in Traumatic Brain Injury Patients." e-CliniC 10, no. 2 (2022): 160. http://dx.doi.org/10.35790/ecl.v10i2.39165.

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Abstract: Traumatic brain injuries (TBI) are determined by the severity of the primary and secondary brain damage. Fibronectin and FOUR score are suggested to be diagnostic and prognostic predictors in patients with traumatic brain injuries (TBI). This study aimed to evaluate the relationship between serum fibronectin level and FOUR score in TBI patients. This was an observational study with a prospective cohort method design, conducted on TBI patients admitted to the emergency room at Prof. Dr. R. D. Kandou Hospital. Serum fibronectin examination and assessment of the level of consciousness d

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Kumar, Shaji, Angela Dispenzieri, Martha Q. Lacy, et al. "Revised Prognostic Staging System for Light Chain Amyloidosis Incorporating Cardiac Biomarkers and Serum Free Light Chain Measurements." Journal of Clinical Oncology 30, no. 9 (2012): 989–95. http://dx.doi.org/10.1200/jco.2011.38.5724.

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Purpose Cardiac involvement predicts poor prognosis in light chain (AL) amyloidosis, and the current prognostic classification is based on cardiac biomarkers troponin-T (cTnT) and N-terminal pro–B-type natriuretic peptide (NT-ProBNP). However, long-term outcome is dependent on the underlying plasma cell clone, and incorporation of clonal characteristics may allow for better risk stratification. Patients and Methods We developed a prognostic model based on 810 patients with newly diagnosed AL amyloidosis, which was further examined in two other datasets: 303 patients undergoing stem-cell transp

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Panda, Nihar Ranjan, Kamal Lochan Mahanta, Jitendra kumar Pati, Soumya Subhashree Satapathy, and Ruchi Bhuyan. "Development of prognostic model and multivariate analysis for breast cancer survival patients using SEER database." Journal of Associated Medical Sciences 57, no. 1 (2024): 67–76. http://dx.doi.org/10.12982/jams.2024.008.

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Background: Many studies employed machine learning (ML) to forecast the prognosis of breast cancer (BC) patients and discovered that the ML model showed high individualized forecasting ability. Breast cancer is the most frequent kind of carcinoma in women globally and ranks as the leading cause of death in women. Objectives: This study intends to use the Surveillance, Epidemiology, and End Results dataset to categorize breast carcinoma cases’ alive and dead conditions. Deep learning and machine learning have been extensively utilized in clinical studies to address various categorization proble

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Lin, Weiyuan, Lifeng Que, Guisen Lin, et al. "Using Machine Learning to Predict Five-Year Reintervention Risk in Type B Aortic Dissection Patients After Thoracic Endovascular Aortic Repair." Journal of Medical Imaging and Health Informatics 11, no. 6 (2021): 1560–67. http://dx.doi.org/10.1166/jmihi.2021.3813.

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Purpose: Type B aortic dissection (TBAD) is a high-risk disease, commonly treated with thoracic endovascular aortic repair (TEVAR). However, for the long-term follow-up, it is associated with a high 5-year reintervention rate for patients after TEVAR. There is no accurate definition of prognostic risk factors for TBAD in medical guidelines, and there is no scientific judgment standard for patients’ quality of life or survival outcome in the next five years in clinical practice. A large amount of medical data features makes prognostic analysis difficult. However, machine learning (ML) permits l

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Ahmed, Nagwa Ramadan, Ahmed Nabil EL-Mazny, Sarah Ahmed Hassan, and Laila Ahmed Rashed. "Prognostic value of serum autotaxin in liver cirrhosis and prediction of hepatocellular carcinoma." Egyptian Journal of Internal Medicine 31, no. 4 (2019): 849–55. http://dx.doi.org/10.4103/ejim.ejim_63_19.

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Abstract Background Autotaxin is a lysophospholipase D related to liver fibrosis; its clinical role in liver cirrhosis is still unknown or limited. In this study we investigate the relation of autotaxin serum levels and prognosis of liver disease and/or prediction of hepatocellular carcinoma (HCC) in hepatitis C virus (HCV) patients. Patients and methods This observational, prospective case–control study included 180 participants, 60 patients with HCV-related liver cirrhosis, 60 HCV noncirrhotic patients, and 60 healthy controls. They were enrolled from inpatients and clinics of a tertiary-car

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Kapoor, Ankita, Sahithi Sonti, Riya Jayesh Patel, et al. "Agrin as a prognostic biomarker in hepatocellular carcinoma." Journal of Clinical Oncology 42, no. 3_suppl (2024): 559. http://dx.doi.org/10.1200/jco.2024.42.3_suppl.559.

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559 Background: Hepatocellular carcinoma (HCC), the predominant form of hepatic cancer is associated with high mortality rates, both in the United States & globally. Alpha-fetoprotein (AFP) & Glypican-3 have been proposed as biomarkers for HCC, although they do not offer any prognostic benefit to model disease progression. Immunotherapy combinations increase patient survival to ~18 months but are not selected based on biomarkers. Agrin, a secreted proteoglycan is frequently overexpressed & secreted in HCC & plays a prominent role in liver tumor microenvironment to promote hepat

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Xie, Hailun, Lishuang Wei, Qiwen Wang, Shuangyi Tang, and Jialiang Gan. "Grading carcinoembryonic antigen levels can enhance the effectiveness of prognostic stratification in patients with colorectal cancer: a single-centre retrospective study." BMJ Open 14, no. 10 (2024): e084219. http://dx.doi.org/10.1136/bmjopen-2024-084219.

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ObjectivesThis study developed a refined carcinoembryonic antigen (CEA) grading system using CEA cut-off points of 5, 20 and 50 ng/mL and to explore the prognostic value of CEA grading in predicting the progression-free survival (PFS) and overall survival (OS) of colorectal cancer (CRC) patients.DesignA retrospective cohort study.SettingFirst Affiliated Hospital of Guangxi Medical University.Participants1107 CRC patients who received surgical treatment.Materials and methodsSurvival analysis was conducted using the Kaplan-Meier method and compared using the log-rank test. A Cox regression model

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Wang, Xin, Yilun Han, Wei Xue, Guangwen Yang, and Guang J. Zhang. "Stable climate simulations using a realistic general circulation model with neural network parameterizations for atmospheric moist physics and radiation processes." Geoscientific Model Development 15, no. 9 (2022): 3923–40. http://dx.doi.org/10.5194/gmd-15-3923-2022.

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Abstract. In climate models, subgrid parameterizations of convection and clouds are one of the main causes of the biases in precipitation and atmospheric circulation simulations. In recent years, due to the rapid development of data science, machine learning (ML) parameterizations for convection and clouds have been demonstrated to have the potential to perform better than conventional parameterizations. Most previous studies were conducted on aqua-planet and idealized models, and the problems of simulation instability and climate drift still exist. Developing an ML parameterization scheme rem

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Bezgin, Tahir, Aziz İnan Çelik, Ali Karagöz, et al. "Prognostic Impact of Modified Glasgow Prognostic Score in Patients with Heart Failure with Mildly Reduced Ejection Fraction." Koşuyolu Heart Journal 25, no. 1 (2022): 6–13. http://dx.doi.org/10.51645/khj.2022.m221.

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Introduction: Inflammation and malnutrition may trigger heart failure development and progression (HF). However, the relationship of the modified Glasgow prognostic score (mGPS), which is derived from C-reactive protein and albumin with mildly reduced ejection fraction HF (HFmrEF), is not well-known. We aimed to determine whether the modified Glasgow prognostic score (mGPS) is helpful for the prediction of all-cause mortality in patients with HFmrEF. Patients and Methods: Patients with HFmrEF admitted to our outpatient clinic between January 2016 and January 2020 were enrolled. All-cause morta

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Kneev, A. Y., M. I. Shkol’nik, O. A. Bogomolov, and G. M. Zharinov. "Prostate specif c antigen density as a prognostic factor in patients with prostate cancer treated with combined hormonal radiation therapy." Siberian journal of oncology 21, no. 3 (2022): 12–23. http://dx.doi.org/10.21294/1814-4861-2022-21-3-12-23.

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Purpose. To evaluate prostate specifc antigen density (PSAD) as a predictor of overall (OS) and cancerspecifc survival (CSS) in patients with prostate cancer (PC) who have undergone combined hormonal-radiation therapy.Material and Methods. In order to assess the prognostic signifcance of PSAD we retrospectively analyzed outcomes of 714 PCa patients who received combined hormonal-radiation therapy at the A.M. Granov Russian Scientifc Center of Radiology and Surgical Technologies, Ministry of Healthcare of Russia, between January 1996 and December 2016. Since the prognosis and management differ

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Dou, Guanhua, Dongkai Shan, Kai Wang, et al. "Integrating Coronary Plaque Information from CCTA by ML Predicts MACE in Patients with Suspected CAD." Journal of Personalized Medicine 12, no. 4 (2022): 596. http://dx.doi.org/10.3390/jpm12040596.

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Conventional prognostic risk analysis in patients undergoing noninvasive imaging is based upon a limited selection of clinical and imaging findings, whereas machine learning (ML) algorithms include a greater number and complexity of variables. Therefore, this paper aimed to explore the predictive value of integrating coronary plaque information from coronary computed tomographic angiography (CCTA) with ML to predict major adverse cardiovascular events (MACEs) in patients with suspected coronary artery disease (CAD). Patients who underwent CCTA due to suspected coronary artery disease with a 30

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Shin, Kabsoo, Joori Kim, Juyeon Park, Ok Ran Kim, Nahyeon Kang, and In-Ho Kim. "Prognostic significance of exosomal programmed death-ligand 1 in advanced gastric cancer patients treated with first-line chemotherapy." Journal of Clinical Oncology 40, no. 4_suppl (2022): 665. http://dx.doi.org/10.1200/jco.2022.40.4_suppl.665.

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665 Background: Recent studies have found that exosomal PD-L1 could be associated with prognosis in several malignancies by its potential immunosuppressive role. Prognostic role of exosomal PD-L1 in advanced gastric cancer patients treated with systemic chemotherapy has not been well explored. The aim of the present study was to explore prognostic and predictive significance of exosomal PD-L1 in advanced gastric cancer patients. Methods: We prospectively collected plasma samples of patients with advanced gastric cancer receiving first-line chemotherapy at pre and post treatment. Combined ultra

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Philip, Mahima Merin, Jessica Watts, Fergus McKiddie, Andy Welch, and Mintu Nath. "Development and Validation of Prognostic Models Using Radiomic Features from Pre-Treatment Positron Emission Tomography (PET) Images in Head and Neck Squamous Cell Carcinoma (HNSCC) Patients." Cancers 16, no. 12 (2024): 2195. http://dx.doi.org/10.3390/cancers16122195.

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High-dimensional radiomics features derived from pre-treatment positron emission tomography (PET) images offer prognostic insights for patients with head and neck squamous cell carcinoma (HNSCC). Using 124 PET radiomics features and clinical variables (age, sex, stage of cancer, site of cancer) from a cohort of 232 patients, we evaluated four survival models—penalized Cox model, random forest, gradient boosted model and support vector machine—to predict all-cause mortality (ACM), locoregional recurrence/residual disease (LR) and distant metastasis (DM) probability during 36, 24 and 24 months o

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Yao, Mylene W. M., Julian Jenkins, Elizabeth T. Nguyen, Trevor Swanson, and Marco Menabrito. "Patient-Centric In Vitro Fertilization Prognostic Counseling Using Machine Learning for the Pragmatist." Seminars in Reproductive Medicine, October 8, 2024. http://dx.doi.org/10.1055/s-0044-1791536.

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AbstractAlthough in vitro fertilization (IVF) has become an extremely effective treatment option for infertility, there is significant underutilization of IVF by patients who could benefit from such treatment. In order for patients to choose to consider IVF treatment when appropriate, it is critical for them to be provided with an accurate, understandable IVF prognosis. Machine learning (ML) can meet the challenge of personalized prognostication based on data available prior to treatment. The development, validation, and deployment of ML prognostic models and related patient counseling report

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Yang, Li-Rong, Zhu-Ying Lin, Qing-Gang Hao, et al. "The prognosis biomarkers based on m6A-related lncRNAs for myeloid leukemia patients." Cancer Cell International 22, no. 1 (2022). http://dx.doi.org/10.1186/s12935-021-02428-3.

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Abstract Background Chronic myeloid leukemia (CML) and acute myeloid leukemia (AML) are two common malignant disorders in leukemia. Although potent drugs are emerging, CML and AML may still relapse after the drug treatment is stopped. N6-methyladenosine (m6A) and lncRNAs play certain roles in the occurrence and development of tumors, but m6A-modified LncRNAs in ML remain to be further investigated. Methods In this study, we extracted and analyzed the TCGA gene expression profile of 151 ML patients and the clinical data. On this basis, we then evaluated the immune infiltration capacity of ML an

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Shen, Jie, Yu Zhou, Junpeng Pei, Dashuai Yang, Kailiang Zhao, and Youming Ding. "Development of prognostic models for advanced multiple hepatocellular carcinoma based on Cox regression, deep learning and machine learning algorithms." Frontiers in Medicine 11 (September 27, 2024). http://dx.doi.org/10.3389/fmed.2024.1452188.

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BackgroundMost patients with multiple hepatocellular carcinoma (MHCC) are at advanced stage once diagnosed, so that clinical treatment and decision-making are quite tricky. The AJCC-TNM system cannot accurately determine prognosis, our study aimed to identify prognostic factors for MHCC and to develop a prognostic model to quantify the risk and survival probability of patients.MethodsEligible patients with HCC were obtained from the Surveillance, Epidemiology, and End Results (SEER) database, and then prognostic models were built using Cox regression, machine learning (ML), and deep learning (

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Liu, Bin, Xiang-Yang Liu, Guo-Ping Wang, and Yi-Xin Chen. "The immune cell infiltration-associated molecular subtypes and gene signature predict prognosis for osteosarcoma patients." Scientific Reports 14, no. 1 (2024). http://dx.doi.org/10.1038/s41598-024-55890-0.

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AbstractHost immune dysregulation involves in the initiation and development of osteosarcoma (OS). However, the exact role of immune cells in OS remains unknown. We aimed to distinguish the molecular subtypes and establish a prognostic model in OS patients based on immunocyte infiltration. The gene expression profile and corresponding clinical feature of OS patients were obtained from TARGET and GSE21257 datasets. MCP-counter and univariate Cox regression analyses were applied to identify immune cell infiltration-related molecular subgroups. Functional enrichment analysis and immunocyte infilt

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Shen, Jie, Dashuai Yang, Yu Zhou, et al. "Development of machine learning models for patients in the high intrahepatic cholangiocarcinoma incidence age group." BMC Geriatrics 24, no. 1 (2024). http://dx.doi.org/10.1186/s12877-024-05154-3.

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Abstract Background Intrahepatic cholangiocarcinoma (ICC) has a poor prognosis and is understudied. Based on the clinical features of patients with ICC, we constructed machine learning models to understand their importance on survival and to accurately determine patient prognosis, aiming to develop reference values to guide physicians in developing more effective treatment plans. Methods This study used machine learning (ML) algorithms to build prediction models using ICC data on 1,751 patients from the SEER (Surveillance, Epidemiology, and End Results) database and 58 hospital cases. The mode

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Zeng, Minyan, Lauren Oakden-Rayner, Alix Bird, et al. "Pre-thrombectomy prognostic prediction of large-vessel ischemic stroke using machine learning: A systematic review and meta-analysis." Frontiers in Neurology 13 (September 8, 2022). http://dx.doi.org/10.3389/fneur.2022.945813.

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IntroductionMachine learning (ML) methods are being increasingly applied to prognostic prediction for stroke patients with large vessel occlusion (LVO) treated with endovascular thrombectomy. This systematic review aims to summarize ML-based pre-thrombectomy prognostic models for LVO stroke and identify key research gaps.MethodsLiterature searches were performed in Embase, PubMed, Web of Science, and Scopus. Meta-analyses of the area under the receiver operating characteristic curves (AUCs) of ML models were conducted to synthesize model performance.ResultsSixteen studies describing 19 models

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Teng, Buwei, Xiaofeng Zhang, Mingshu Ge, Miao Miao, Wei Li, and Jun Ma. "Personalized three-year survival prediction and prognosis forecast by interpretable machine learning for pancreatic cancer patients: a population-based study and an external validation." Frontiers in Oncology 14 (October 21, 2024). http://dx.doi.org/10.3389/fonc.2024.1488118.

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PurposeThe overall survival of patients with pancreatic cancer is extremely low. We aimed to establish machine learning (ML) based model to accurately predict three-year survival and prognosis of pancreatic cancer patients.MethodsWe analyzed pancreatic cancer patients from the Surveillance, Epidemiology, and End Results (SEER) database between 2000 and 2021. Univariate and multivariate logistic analysis were employed to select variables. Recursive Feature Elimination (RFE) method based on 6 ML algorithms was utilized in feature selection. To construct predictive model, 13 ML algorithms were ev

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Guo, Kun, Bo Zhu, Lei Zha, et al. "Interpretable prediction of stroke prognosis: SHAP for SVM and nomogram for logistic regression." Frontiers in Neurology 16 (March 4, 2025). https://doi.org/10.3389/fneur.2025.1522868.

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BackgroundIschemic Stroke (IS) stands as a leading cause of mortality and disability globally, with an anticipated increase in IS-related fatalities by 2030. Despite therapeutic advancements, many patients still lack effective interventions, underscoring the need for improved prognostic assessment tools. Machine Learning (ML) models have emerged as promising tools for predicting stroke prognosis, surpassing traditional methods in accuracy and speed.ObjectiveThe aim of this study was to develop and validate ML algorithms for predicting the 6-month prognosis of patients with Acute Cerebral Infar

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Kinoshita, Yoshiaki, Takato Ikeda, Takuto Miyamura, et al. "A proposed prognostic prediction score for pleuroparenchymal fibroelastosis." Respiratory Research 22, no. 1 (2021). http://dx.doi.org/10.1186/s12931-021-01810-z.

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Abstract Background Clinical course of pleuroparenchymal fibroelastosis (PPFE) shows considerable variation among patients, but there is no established prognostic prediction model for PPFE. Methods The prediction model was developed using retrospective data from two cohorts: our single-center cohort and a nationwide multicenter cohort involving 21 institutions. Cox regression analyses were used to identify prognostic factors. The total score was defined as the weighted sum of values for the selected variables. The performance of the prediction models was evaluated by Harrell’s concordance inde

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Fang, Yutong, Rongji Zheng, Yefeng Xiao, Qunchen Zhang, Junpeng Liu, and Jundong Wu. "Machine learning-based diagnostic and prognostic models for breast cancer: a new frontier on the clinical application of natural killer cell-related gene signatures in precision medicine." Frontiers in Immunology 16 (May 27, 2025). https://doi.org/10.3389/fimmu.2025.1581982.

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BackgroundBreast cancer (BC) remains a leading cause of cancer-related mortality among women worldwide. Natural killer (NK) cells play a crucial role in the innate immune system and exhibit significant anti-tumor activity. However, the role of NK cell-related genes (NRGs) in BC diagnosis and prognosis remains underexplored. With the advent of machine learning (ML) techniques, predictive modeling based on NRGs may offer a new avenue for precision oncology.MethodsWe collected transcriptomic and clinical data from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. Differe

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Wang, Cong, Hongwei Li, Hongye Yang, and Yingwei Xue. "Machine learning-based prediction of five-year all-cause mortality in patients with mixed gastric cancer." Holistic Integrative Oncology 4, no. 1 (2025). https://doi.org/10.1007/s44178-025-00159-3.

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Abstract Background Machine learning (ML) is increasingly being used to predict prognosis. This study aimed to establish and compare various ML models for predicting five-year all-cause mortality in patients with mixed gastric cancer, focusing on identifying significant prognostic features. Methods We developed five ML models using a follow-up database of mixed gastric cancer patients. The model with the highest performance, the Light Gradient Boosting Machine (LGBM), was selected to predict five-year all-cause mortality. The log-rank test was employed to evaluate the divergence of Kaplan–Meie

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Wang, Jing, Kai Wang, Kangjie Wang, et al. "A machine learning-based prognostic stratification of locoregional interventional therapies for patients with colorectal cancer liver metastases: a real-world study." Therapeutic Advances in Medical Oncology 17 (June 2025). https://doi.org/10.1177/17588359251353084.

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Background: Colorectal cancer liver metastases (CRLM) represent a major cause of mortality in advanced colorectal cancer, with intra-arterial interventional therapy (IAIT) playing an increasingly important role in multidisciplinary management. This study aims to develop a machine learning (ML)-based prognostic model to predict survival outcomes in unresectable colorectal cancer liver metastases (uCRLM) patients undergoing IAIT treatment, enabling improved risk assessment. Design: A retrospective study. Objectives: This study aims to explore the effect of IAIT on the survival of patients with u

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Wang, Kai, Tao Hong, Wencai Liu, et al. "Development and validation of a machine learning-based prognostic risk stratification model for acute ischemic stroke." Scientific Reports 13, no. 1 (2023). http://dx.doi.org/10.1038/s41598-023-40411-2.

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AbstractAcute ischemic stroke (AIS) is a most prevalent cause of serious long-term disability worldwide. Accurate prediction of stroke prognosis is highly valuable for effective intervention and treatment. As such, the present retrospective study aims to provide a reliable machine learning-based model for prognosis prediction in AIS patients. Data from AIS patients were collected retrospectively from the Second Affiliated Hospital of Xuzhou Medical University between August 2017 and July 2019. Independent prognostic factors were identified by univariate and multivariate logistic analysis and u

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Fan, Kaiting, Wenya Cao, Hong Chang, and Fei Tian. "Predicting prognosis in patients with stroke treated with intravenous alteplase through blood pressure changes: A machine learning‐based approach." Journal of Clinical Hypertension, October 16, 2023. http://dx.doi.org/10.1111/jch.14732.

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AbstractThe use of machine learning (ML) in predicting disease prognosis has increased, and researchers have adopted different methods for variable selection to optimize early screening for AIS to determine its prognosis as soon as possible. We aimed to improve the understanding of the predictors of poor functional outcome at three months after discharge in AIS patients treated with intravenous thrombolysis and to construct a highly effective prognostic model to improve prediction accuracy. And four ML methods (random forest, support vector machine, naive Bayesian, and logistic regression) wer

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Hill, Holly Ann, Preetesh Jain, Chi Young Ok, et al. "Integrative Prognostic Machine-Learning Models in Mantle Cell Lymphoma." Cancer Research Communications, July 11, 2023. http://dx.doi.org/10.1158/2767-9764.crc-23-0083.

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Abstract Patients with mantle cell lymphoma (MCL), an incurable B-cell malignancy, benefit from accurate pretreatment disease stratification. We curated an extensive database of 862 patients diagnosed between 2014 and 2022. A machine learning (ML) gradient-boosted model incorporated baseline features from clinicopathological, cytogenetic, and genomic data with high predictive power discriminating between patients with indolent or responsive MCL and those with aggressive disease (AUC ROC = 0.83). Additionally, we utilized the gradient-boosted framework as a robust feature selection method for m

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