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dc.contributor.authorBazoukis, George
dc.contributor.authorStavrakis, Stavros
dc.contributor.authorZhou, Jiandong
dc.contributor.authorBollepalli, Sandeep Chandra
dc.contributor.authorTse, Gary
dc.contributor.authorZhang, Qingpeng
dc.contributor.authorSingh, Jagmeet P
dc.contributor.authorArmoundas, Antonis A.
dc.date.accessioned2021-01-25T16:56:43Z
dc.date.available2021-01-25T16:56:43Z
dc.date.issued2020-07
dc.identifier.issn1382-4147
dc.identifier.issn1573-7322
dc.identifier.urihttps://hdl.handle.net/1721.1/129544
dc.description.abstractMachine 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.en_US
dc.publisherSpringer Science and Business Media LLCen_US
dc.relation.isversionofhttps://doi.org/10.1007/s10741-020-10007-3en_US
dc.rightsArticle is made available in accordance with the publisher's policy and may be subject to US copyright law. Please refer to the publisher's site for terms of use.en_US
dc.sourceSpringer USen_US
dc.titleMachine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic reviewen_US
dc.typeArticleen_US
dc.identifier.citationBazoukis, George et al. "Machine learning versus conventional clinical methods in guiding management of heart failure patients—a systematic review." Heart Failure Reviews 26, 1 (January 2021): 23–34 © 2020 Springer Science Business Media, LLC, part of Springer Natureen_US
dc.contributor.departmentMassachusetts Institute of Technology. Institute for Medical Engineering & Scienceen_US
dc.contributor.departmentMassachusetts Institute of Technology. Clinical Research Centeren_US
dc.relation.journalHeart Failure Reviewsen_US
dc.eprint.versionAuthor's final manuscripten_US
dc.type.urihttp://purl.org/eprint/type/JournalArticleen_US
eprint.statushttp://purl.org/eprint/status/PeerRevieweden_US
dc.date.updated2020-12-29T04:19:21Z
dc.language.rfc3066en
dc.rights.holderSpringer Science+Business Media, LLC, part of Springer Nature
dspace.embargo.termsY
dspace.date.submission2020-12-29T04:19:20Z
mit.journal.volume26en_US
mit.journal.issue1en_US
mit.licensePUBLISHER_POLICY
mit.metadata.statusComplete


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