AIMC Topic: Risk Assessment
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Machine Learning Multimodal Model for Delirium Risk Stratification.
JAMA network open
May 1, 2025
IMPORTANCE: Automating the identification of risk for developing hospital delirium with models that use machine learning (ML) could facilitate more rapid prevention, identification, and treatment of delirium. However, there are very few reports on th...
Multimodal Visualization and Explainable Machine Learning-Driven Markers Enable Early Identification and Prognosis Prediction for Symptomatic Aortic Stenosis and Heart Failure With Preserved Ejection Fraction After Transcatheter Aortic Valve Replacement: Multicenter Cohort Study.
Journal of medical Internet research
May 1, 2025
BACKGROUND: Currently, there is a paucity of literature addressing personalized risk stratification using multimodal data in patients with symptomatic aortic stenosis and heart failure with preserved ejection fraction (HFpEF) following transcatheter ...
Development and validation of a machine learning-based risk model for metastatic disease in nmCRPC patients: a tumor marker prognostic study.
International journal of surgery (London, England)
May 1, 2025
BACKGROUND: Nonmetastatic castration-resistant prostate cancer (nmCRPC) is a clinical challenge due to the high progression rate to metastasis and mortality. To date, no prognostic model has been developed to predict the metastatic probability for nm...
An artificial intelligence interpretable tool to predict risk of deep vein thrombosis after endovenous thermal ablation.
Journal of vascular surgery. Venous and lymphatic disorders
Apr 30, 2025
OBJECTIVE: Endovenous thermal ablation (EVTA) stands as one of the primary treatments for superficial venous insufficiency. Concern exists about the potential for thromboembolic complications following this procedure. Although rare, those complicatio...
Reproducibility of Results
Ablation Techniques
Risk Factors
Aged
Adult
Decision Support Techniques
Venous Thrombosis
Humans
Risk Assessment
Databases, Factual
Predictive Value of Tests
Artificial Intelligence
Time Factors
Male
Retrospective Studies
Endovascular Procedures
Machine Learning
Treatment Outcome
Female
Venous Insufficiency
Middle Aged
Unveiling PFAS hazard in European surface waters using an interpretable machine-learning model.
Environment international
Apr 30, 2025
Per- and polyfluoroalkyl substances (PFAS), commonly known as "forever chemicals", are ubiquitous in surface waters and potentially threaten human health and ecosystems. Despite extensive monitoring efforts, PFAS risk in European surface waters remai...
Using machine learning involving diagnoses and medications as a risk prediction tool for post-acute sequelae of COVID-19 (PASC) in primary care.
BMC medicine
Apr 30, 2025
BACKGROUND: The aim of our study was to determine whether the application of machine learning could predict PASC by using diagnoses from primary care and prescribed medication 1 year prior to PASC diagnosis.
Machine learning-based prediction of postoperative pancreatic fistula after laparoscopic pancreaticoduodenectomy.
BMC surgery
Apr 30, 2025
BACKGROUND: Clinically relevant postoperative pancreatic fistula (CR-POPF) following laparoscopic pancreaticoduodenectomy (LPD) is a critical complication that significantly worsens patient outcomes. However, the heterogeneity of its risk factors and...
Development and evaluation of a machine learning model to predict acute care for opioid use disorder among Medicaid enrollees engaged in a community-based treatment program.
Addiction (Abingdon, England)
Apr 29, 2025
AIMS: To develop machine-learning algorithms for predicting the risk of a hospitalization or emergency department (ED) visit for opioid use disorder (OUD) (i.e. OUD acute events) in Pennsylvania Medicaid enrollees in the Opioid Use Disorder Centers o...
Intelligent predictive risk assessment and management of sarcopenia in chronic disease patients using machine learning and a web-based tool.
European journal of medical research
Apr 29, 2025
BACKGROUND: Individuals with chronic diseases are at higher risk of sarcopenia, and precise prediction is essential for its prevention. This study aims to develop a risk scoring model using longitudinal data to predict the probability of sarcopenia i...
A deep-learning algorithm using real-time collected intraoperative vital sign signals for predicting acute kidney injury after major non-cardiac surgeries: A modelling study.
PLoS medicine
Apr 29, 2025
BACKGROUND: Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics.
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