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An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review
Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We t...
Autores principales: | , , , , , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
MDPI
2022
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496386/ https://www.ncbi.nlm.nih.gov/pubmed/36140289 http://dx.doi.org/10.3390/biomedicines10092188 |
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author | Błaziak, Mikołaj Urban, Szymon Wietrzyk, Weronika Jura, Maksym Iwanek, Gracjan Stańczykiewicz, Bartłomiej Kuliczkowski, Wiktor Zymliński, Robert Pondel, Maciej Berka, Petr Danel, Dariusz Biegus, Jan Siennicka, Agnieszka |
author_facet | Błaziak, Mikołaj Urban, Szymon Wietrzyk, Weronika Jura, Maksym Iwanek, Gracjan Stańczykiewicz, Bartłomiej Kuliczkowski, Wiktor Zymliński, Robert Pondel, Maciej Berka, Petr Danel, Dariusz Biegus, Jan Siennicka, Agnieszka |
author_sort | Błaziak, Mikołaj |
collection | PubMed |
description | Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management. |
format | Online Article Text |
id | pubmed-9496386 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | MDPI |
record_format | MEDLINE/PubMed |
spelling | pubmed-94963862022-09-23 An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review Błaziak, Mikołaj Urban, Szymon Wietrzyk, Weronika Jura, Maksym Iwanek, Gracjan Stańczykiewicz, Bartłomiej Kuliczkowski, Wiktor Zymliński, Robert Pondel, Maciej Berka, Petr Danel, Dariusz Biegus, Jan Siennicka, Agnieszka Biomedicines Systematic Review Heart failure (HF) is one of the leading causes of mortality and hospitalization worldwide. The accurate prediction of mortality and readmission risk provides crucial information for guiding decision making. Unfortunately, traditional predictive models reached modest accuracy in HF populations. We therefore aimed to present predictive models based on machine learning (ML) techniques in HF patients that were externally validated. We searched four databases and the reference lists of the included papers to identify studies in which HF patient data were used to create a predictive model. Literature screening was conducted in Academic Search Ultimate, ERIC, Health Source Nursing/Academic Edition and MEDLINE. The protocol of the current systematic review was registered in the PROSPERO database with the registration number CRD42022344855. We considered all types of outcomes: mortality, rehospitalization, response to treatment and medication adherence. The area under the receiver operating characteristic curve (AUC) was used as the comparator parameter. The literature search yielded 1649 studies, of which 9 were included in the final analysis. The AUCs for the machine learning models ranged from 0.6494 to 0.913 in independent datasets, whereas the AUCs for statistical predictive scores ranged from 0.622 to 0.806. Our study showed an increasing number of ML predictive models concerning HF populations, although external validation remains infrequent. However, our findings revealed that ML approaches can outperform conventional risk scores and may play important role in HF management. MDPI 2022-09-05 /pmc/articles/PMC9496386/ /pubmed/36140289 http://dx.doi.org/10.3390/biomedicines10092188 Text en © 2022 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). |
spellingShingle | Systematic Review Błaziak, Mikołaj Urban, Szymon Wietrzyk, Weronika Jura, Maksym Iwanek, Gracjan Stańczykiewicz, Bartłomiej Kuliczkowski, Wiktor Zymliński, Robert Pondel, Maciej Berka, Petr Danel, Dariusz Biegus, Jan Siennicka, Agnieszka An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review |
title | An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review |
title_full | An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review |
title_fullStr | An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review |
title_full_unstemmed | An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review |
title_short | An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review |
title_sort | artificial intelligence approach to guiding the management of heart failure patients using predictive models: a systematic review |
topic | Systematic Review |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9496386/ https://www.ncbi.nlm.nih.gov/pubmed/36140289 http://dx.doi.org/10.3390/biomedicines10092188 |
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