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AuthorBoughorbel, Sabr
AuthorHimeur, Yassine
AuthorSalman, Huseyin Enes
AuthorBensaali, Faycal
AuthorFarooq, Faisal
AuthorYalcin, Huseyin Cagatay
Available date2023-08-29T05:01:31Z
Publication Date2022-04-22
Publication NamePredicting Heart Failure: Invasive, Non‐Invasive,Machine Learning and Artificial Intelligence Based Methods
Identifierhttp://dx.doi.org/10.1002/9781119813040.ch8
CitationBoughorbel, S., Himeur, Y., Salman, H.E., Bensaali, F., Farooq, F. and Yalcin, H.C. (2022). Applications of Machine Learning for Predicting Heart Failure. In Predicting Heart Failure (eds K.K. Sadasivuni, H.M. Ouakad, S. Al-Maadeed, H.C. Yalcin and I.B. Bahadur). https://doi.org/10.1002/9781119813040.ch8
URIhttp://hdl.handle.net/10576/46858
AbstractHeart Failure is a major health burden for healthcare systems worldwide. Early diagnosis, prediction and management of patients with these conditions are critical to improve patient health outcome. The availability of large datasets from different sources can be leveraged to build machine learning models that can empower clinicians by providing early warnings and insightful information on the underlying conditions of the patients. In this chapter, we review research work on the application of machine learning methods for the diagnosis and prediction of heart failure, and readmission risk scoring. We present recent work on the use of different clinical modalities such as pathology images, echocardiography reports, electronic health records for building predictive models for heart failure diagnosis and prediction. We will cover the model details from traditional machine learning methods as well as from deep learning. Furthermore, we give a summary of the results and performance of these techniques.
SponsorQatar University
Languageen
PublisherWiley
SubjectHeart failure
Machine Learning
prediction
TitleApplications of Machine Learning for Predicting Heart Failure
TypeBook chapter
Pagination171-188
EISBN9781119813040
dc.accessType Full Text


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