AIMC Topic: Risk Assessment

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Assessment of the risk of osteoporotic bone fracture in postmenopausal women using machine learning methods.

Scientific reports
The main objective of osteoporosis management is to prevent osteoporotic fractures. Using machine learning methods, new risk variables can be identified to enhance the ability to identify women with osteoporosis who are at an increased risk of bone f...

Coagulation Risk Prediction in Patients With Liver Failure: Integrated Meta-Analysis and Machine Learning Model Study.

JMIR medical informatics
BACKGROUND: Liver failure often results in significant coagulation dysfunction, which is a major complication. Artificial liver support systems (ALSS) have been used to ameliorate coagulation parameters, but the dynamic nature of these improvements a...

Machine Learning-Based Prediction of In-Hospital Falls in Adult Inpatients: Retrospective Observational Multicenter Study.

JMIR medical informatics
BACKGROUND: Falls among hospitalized patients are a critical issue that often leads to prolonged hospital stays and increased health care costs. Traditional fall risk assessments typically rely on standardized scoring systems; however, these may fail...

ECG-based deep learning for chronic kidney disease detection and cardiovascular risk prediction.

BMC medical informatics and decision making
BACKGROUND: Chronic kidney disease (CKD) is a global health burden with low awareness among both patients and healthcare providers. Deep learning models (DLMs) have shown promise in interpreting electrocardiograms (ECGs) for various disease and may o...

Development of explainable machine learning models to predict side effects in patients with rheumatoid arthritis taking methotrexate treatment: a nationwide multicentre cohort study.

BMJ open
OBJECTIVES: Methotrexate (MTX) effectively controls rheumatoid arthritis (RA) but often leads to side effects (SE) such as gastrointestinal (GI) issues, liver toxicity and bone marrow suppression. To develop clinically interpretable machine learning ...

Accuracy is not enough: explainable boosting machine model and identification of candidate biomarkers for real-time sepsis risk assessment in the emergency department.

BMC emergency medicine
BACKGROUND: Sepsis poses a significant threat in emergency settings, necessitating tools for early and interpretable risk assessment. This study aimed to develop a robust explainable boosting machine (EBM) model, one of the explainable artificial int...

Prioritization of patients at risk of heart attack using a novel full-objective ITARA based on Random Forest and Decision tree.

Scientific reports
Heart attacks remain a major cause of morbidity and mortality, particularly among middle-aged and older adults, often aggravated by unhealthy lifestyles and limited preventive care. Early identification and prioritization of at-risk individuals are e...

AI-Powered Thermography for Diabetic Foot Risk Stratification: Multicenter Cross-Sectional Study.

JMIR formative research
BACKGROUND: Diabetic foot complications are among the most severe and costly outcomes associated with diabetes, with high prevalence particularly in the Middle East and North Africa region. Current screening tools are often limited by subjectivity, i...