AIMC Topic: Risk Assessment

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Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis.

International journal of medical informatics
BACKGROUND: Machine learning (ML) models have been constructed to predict the risk of in-hospital mortality in patients with myocardial infarction (MI). Due to diverse ML models and modeling variables, along with the significant imbalance in data, th...

Ensemble-learning approach improves fracture prediction using genomic and phenotypic data.

Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED: This study presents an innovative ensemble machine learning model integrating genomic and clinical data to enhance the prediction of major osteoporotic fractures in older men. The Super Learner (SL) model achieved superior performance (AU...

Retinal vein occlusion risk prediction without fundus examination using a no-code machine learning tool for tabular data: a nationwide cross-sectional study from South Korea.

BMC medical informatics and decision making
BACKGROUND: Retinal vein occlusion (RVO) is a leading cause of vision loss globally. Routine health check-up data-including demographic information, medical history, and laboratory test results-are commonly utilized in clinical settings for disease r...

Prediction of postpartum depression in women: development and validation of multiple machine learning models.

Journal of translational medicine
BACKGROUND: Postpartum depression (PPD) is a significant public health issue. This study aimed to develop and validate machine learning (ML) models using biopsychosocial predictors to predict the risk of PPD for perinatal women and to provide several...

Machine learning enhanced immunologic risk assessments for solid organ transplantation.

Scientific reports
The purpose of this study was to enhance the prediction of solid-organ recipient and donor crossmatch compatibility by applying machine learning (ML). Prediction of crossmatch compatibility is complex and requires an understanding of the recipient an...

Multi-Omics Deep-Learning Prediction of Homologous Recombination Deficiency-Like Phenotype Improved Risk Stratification and Guided Therapeutic Decisions in Gynecological Cancers.

IEEE journal of biomedical and health informatics
Homologous recombination deficiency (HRD) is a well-recognized important biomarker in determining the clinical benefits of platinum-based chemotherapy and PARP inhibitor therapy for patients diagnosed with gynecologic cancers. Accurate prediction of ...