Machine learning in heart failure: ready for prime time

被引：88

作者：

Awan, Saqib Ejaz ^{[1
]}

Sohel, Ferdous ^{[2
]}

Sanfilippo, Frank Mario ^{[3
]}

Bennamoun, Mohammed ^{[1
]}

Dwivedi, Girish ^{[4
,5
]}

机构：

[1] Univ Western Australia, Sch Comp Sci & Software Engn, Perth, WA, Australia

[2] Murdoch Univ, Sch Engn & Informat Technol, Murdoch, WA, Australia

[3] Univ Western Australia, Sch Populat & Global Hlth, Perth, WA, Australia

[4] Harry Perkins Inst Med Res, Perth, WA, Australia

[5] Univ Western Australia, Fiona Stanley Hosp, Perth, WA, Australia

来源：

CURRENT OPINION IN CARDIOLOGY | 2018年 / 33卷 / 02期

关键词：

artificial intelligence; deep learning; diagnosis; heart failure; machine learning; medication adherence; CLASSIFICATION; PREDICTION; DIAGNOSIS;

D O I：

10.1097/HCO.0000000000000491

中图分类号：

R5 [内科学];

学科分类号：

100201 [内科学];

摘要：

Purpose of review The aim of this review is to present an up-to-date overview of the application of machine learning methods in heart failure including diagnosis, classification, readmissions and medication adherence. Recent findings Recent studies have shown that the application of machine learning techniques may have the potential to improve heart failure outcomes and management, including cost savings by improving existing diagnostic and treatment support systems. Recently developed deep learning methods are expected to yield even better performance than traditional machine learning techniques in performing complex tasks by learning the intricate patterns hidden in big medical data. Summary The review summarizes the recent developments in the application of machine and deep learning methods in heart failure management.

引用

页码：190 / 195

页数：6

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