A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data
BACKGROUND: Heart failure is one of the leading causes of hospitalization in the United States. Advances in big data solutions allow for storage, management, and mining of large volumes of structured and semi-structured data, such as complex healthcare data. Applying these advances to complex healthcare data has led to the development of risk prediction models to help identify patients who would benefit most from disease management programs in an effort to reduce readmissions and healthcare cost
Record details
Published: 21 June 2018
Source: OpenAlex
Category: Research
Topics: Healthcare
Retrieved: 14 July 2026
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How to cite this record
ethics.ai (21 June 2018), “A machine learning model to predict the risk of 30-day readmissions in patients with heart failure: a retrospective analysis of electronic medical records data,” evidence record 8330, https://ethics.ai/record/8330 (originally published by OpenAlex).
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