Clinically Interpretable Machine Learning Models for Early Prediction of Mortality in Older Patients with Multiple Organ Dysfunction Syndrome (MODS): An International Multicenter Retrospective Study.
Xiaoli LiuClark DumontierPan HuChao LiuWesley YeungZhi MaoVanda HoThoral PjPo-Chih KuoJie HuDeyu LiDesen CaoRoger G MarkFei Hu ZhouZhengbo ZhangLeo Anthony CeliPublished in: The journals of gerontology. Series A, Biological sciences and medical sciences (2022)
Our models integrate data spanning physiologic and geriatric-relevant variables that outperform existing scores used in older adults with MODS, which represents a proof of concept of how machine learning can streamline data analysis for busy ICU clinicians to potentially optimize prognostication and decision making.
Keyphrases
- data analysis
- machine learning
- decision making
- big data
- community dwelling
- artificial intelligence
- middle aged
- physical activity
- intensive care unit
- cardiovascular events
- palliative care
- oxidative stress
- electronic health record
- case report
- cross sectional
- cardiovascular disease
- hip fracture
- mechanical ventilation
- clinical trial
- coronary artery disease