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المؤلفBoughorbel, Sabr
المؤلفHimeur, Yassine
المؤلفSalman, Huseyin Enes
المؤلفBensaali, Faycal
المؤلفFarooq, Faisal
المؤلفYalcin, Huseyin Cagatay
تاريخ الإتاحة2023-08-29T05:01:31Z
تاريخ النشر2022-04-22
اسم المنشورPredicting Heart Failure: Invasive, Non‐Invasive,Machine Learning and Artificial Intelligence Based Methods
المعرّفhttp://dx.doi.org/10.1002/9781119813040.ch8
الاقتباسBoughorbel, 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
معرّف المصادر الموحدhttp://hdl.handle.net/10576/46858
الملخصHeart 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.
راعي المشروعQatar University
اللغةen
الناشرWiley
الموضوعHeart failure
Machine Learning
prediction
العنوانApplications of Machine Learning for Predicting Heart Failure
النوعBook chapter
الصفحات171-188
الترقيم الدولي الموحد للكتاب (إلكتروني) 9781119813040
dc.accessType Full Text


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