Heart Failure Diagnosis, Readmission, and Mortality Prediction Using Machine Learning and Artificial Intelligence Models
| Autor: | Michael K. Pasque, Douglas L. Mann, Francis Loh, Philip R. O. Payne, Aixia Guo |
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| Rok vydání: | 2020 |
| Předmět: |
Computer science
business.industry 030204 cardiovascular system & hematology medicine.disease Machine learning computer.software_genre Imbalanced data 03 medical and health sciences Identification (information) 0302 clinical medicine Electronic health record Heart failure medicine General Earth and Planetary Sciences 030212 general & internal medicine Artificial intelligence Mortality prediction business Outcome prediction computer |
| Zdroj: | Current Epidemiology Reports. 7:212-219 |
| ISSN: | 2196-2995 |
| DOI: | 10.1007/s40471-020-00259-w |
| Popis: | Purpose of Review One in five people will develop heart failure (HF), and 50% of HF patients die in 5 years. The HF diagnosis, readmission, and mortality prediction are essential to develop personalized prevention and treatment plans. This review summarizes recent findings and approaches of machine learning models for HF diagnostic and outcome prediction using electronic health record (EHR) data. Recent Findings A set of machine learning models have been developed for HF diagnostic and outcome prediction using diverse variables derived from EHR data, including demographic, medical note, laboratory, and image data, and achieved expert-comparable prediction results. Summary Machine learning models can facilitate the identification of HF patients, as well as accurate patient-specific assessment of their risk for readmission and mortality. Additionally, novel machine learning techniques for integration of diverse data and improvement of model predictive accuracy in imbalanced data sets are critical for further development of these promising modeling methodologies. |
| Databáze: | OpenAIRE |
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