AIMC Topic: Mortality
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Reconfiguration of uncertainty: Introducing AI for prediction of mortality at the emergency department.
Social science & medicine (1982)
Sep 6, 2024
The promise behind many advanced digital technologies in healthcare is to provide novel and accurate information, aiding medical experts to navigate and, ultimately, decrease uncertainty in their clinical work. However, sociological studies have star...
Random survival forest for predicting the combined effects of multiple physiological risk factors on all-cause mortality.
Scientific reports
Jul 6, 2024
Understanding the combined effects of risk factors on all-cause mortality is crucial for implementing effective risk stratification and designing targeted interventions, but such combined effects are understudied. We aim to use survival-tree based ma...
Deep learning model for the prediction of all-cause mortality among long term care people in China: a prospective cohort study.
Scientific reports
Jun 25, 2024
This study aimed to develop a deep learning model to predict the risk stratification of all-cause death for older people with disability, providing guidance for long-term care plans. Based on the government-led long-term care insurance program in a p...
Deep learning-based prediction of one-year mortality in Finland is an accurate but unfair aging marker.
Nature aging
Jun 24, 2024
Short-term mortality risk, which is indicative of individual frailty, serves as a marker for aging. Previous age clocks focused on predicting either chronological age or longer-term mortality. Aging clocks predicting short-term mortality are lacking ...
Machine learning prediction of one-year mortality after percutaneous coronary intervention in acute coronary syndrome patients.
International journal of cardiology
May 20, 2024
BACKGROUND: Machine learning (ML) models have the potential to accurately predict outcomes and offer novel insights into inter-variable correlations. In this study, we aimed to design ML models for the prediction of 1-year mortality after percutaneou...
Towards proactive palliative care in oncology: developing an explainable EHR-based machine learning model for mortality risk prediction.
BMC palliative care
May 20, 2024
BACKGROUND: Ex-ante identification of the last year in life facilitates a proactive palliative approach. Machine learning models trained on electronic health records (EHR) demonstrate promising performance in cancer prognostication. However, gaps in ...
Self-Supervised Machine Learning to Characterize Step Counts from Wrist-Worn Accelerometers in the UK Biobank.
Medicine and science in sports and exercise
May 15, 2024
PURPOSE: Step count is an intuitive measure of physical activity frequently quantified in health-related studies; however, accurate step counting is difficult in the free-living environment, with error routinely above 20% in wrist-worn devices agains...
Physical performance strongly predicts all-cause mortality risk in a real-world population of older diabetic patients: machine learning approach for mortality risk stratification.
Frontiers in endocrinology
Apr 30, 2024
BACKGROUND: Prognostic risk stratification in older adults with type 2 diabetes (T2D) is important for guiding decisions concerning advance care planning.
Looking Beyond Mortality Prediction: Primary Care Physician Views of Patients' Palliative Care Needs Predicted by a Machine Learning Tool.
Applied clinical informatics
Apr 18, 2024
OBJECTIVES: To assess primary care physicians' (PCPs) perception of the need for serious illness conversations (SIC) or other palliative care interventions in patients flagged by a machine learning tool for high 1-year mortality risk.
Development, validation, and transportability of several machine-learned, non-exercise-based VO prediction models for older adults.
Journal of sport and health science
Feb 29, 2024
BACKGROUND: There exist few maximal oxygen uptake (VO) non-exercise-based prediction equations, fewer using machine learning (ML), and none specifically for older adults. Since direct measurement of VO is infeasible in large epidemiologic cohort stud...
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