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- W4229074507 abstract "This chapter presents a brief definition of heart failure (HF) along with the various classifications, evaluation, and diagnosis of HF. HF imposes a significant burden on both patients and healthcare systems, with costly hospitalizations and high mortality rates. A large number of studies focus on developing models for jointly predicting adverse events, that is, mortality or rehospitalization in patients with HF. The chapter demonstrates an extended review of the state of the art of applications of machine learning (ML) in HF diagnosis, severity estimation, and the prediction of adverse events (mortality or rehospitalization). The models are based on ML approaches and consist of data cleaning, feature selection, class balancing, and classification steps. The HF severity estimation and the adverse events prediction are embedded in the Hearten Knowledge Management System, which offers decision support in HF management." @default.
- W4229074507 created "2022-05-08" @default.
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- W4229074507 date "2022-04-22" @default.
- W4229074507 modified "2023-10-16" @default.
- W4229074507 title "Machine Learning Techniques for Predicting and Managing Heart Failure" @default.
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- W4229074507 doi "https://doi.org/10.1002/9781119813040.ch9" @default.