About: Machine learning versus conventional clinical methods in guiding management of heart failure patients

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About: Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : covidontheweb.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review
Creator	Zhang, Qingpeng Tse, Gary Bazoukis, George Singh, Jagmeet Zhou, Jiandong Armoundas, Antonis Stavrakis, Stavros Bollepalli, Chandra Sandeep, &
Source	PMC
abstract	Machine learning (ML) algorithms “learn” information directly from data, and their performance improves proportionally with the number of high-quality samples. The aim of our systematic review is to present the state of the art regarding the implementation of ML techniques in the management of heart failure (HF) patients. We manually searched MEDLINE and Cochrane databases as well the reference lists of the relevant review studies and included studies. Our search retrieved 122 relevant studies. These studies mainly refer to (a) the role of ML in the classification of HF patients into distinct categories which may require a different treatment strategy, (b) discrimination of HF patients from the healthy population or other diseases, (c) prediction of HF outcomes, (d) identification of HF patients from electronic records and identification of HF patients with similar characteristics who may benefit form a similar treatment strategy, (e) supporting the extraction of important data from clinical notes, and (f) prediction of outcomes in HF populations with implantable devices (left ventricular assist device, cardiac resynchronization therapy). We concluded that ML techniques may play an important role for the efficient construction of methodologies for diagnosis, management, and prediction of outcomes in HF patients. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (10.1007/s10741-020-10007-3) contains supplementary material, which is available to authorized users.
has issue date	2020-07-27(xsd:dateTime)
bibo:doi	10.1007/s10741-020-10007-3
has license	no-cc
sha1sum (hex)	32ee2706625759966b9cd86976ccc58d35c59638
schema:url	https://doi.org/10.1007/s10741-020-10007-3
resource representing a document's title	Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review
has PubMed Central identifier	PMC7384870
schema:publication	Heart Fail Rev
resource representing a document's body	covid:32ee2706625759966b9cd86976ccc58d35c59638#body_text
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