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...
| Main Authors: | , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
MDPI AG
2022-09-01
|
| Series: | Biomedicines |
| Subjects: | |
| Online Access: | https://www.mdpi.com/2227-9059/10/9/2188 |
| _version_ | 1827663220689600512 |
|---|---|
| author | Mikołaj Błaziak Szymon Urban Weronika Wietrzyk Maksym Jura Gracjan Iwanek Bartłomiej Stańczykiewicz Wiktor Kuliczkowski Robert Zymliński Maciej Pondel Petr Berka Dariusz Danel Jan Biegus Agnieszka Siennicka |
| author_facet | Mikołaj Błaziak Szymon Urban Weronika Wietrzyk Maksym Jura Gracjan Iwanek Bartłomiej Stańczykiewicz Wiktor Kuliczkowski Robert Zymliński Maciej Pondel Petr Berka Dariusz Danel Jan Biegus Agnieszka Siennicka |
| author_sort | Mikołaj Błaziak |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-10T00:39:48Z |
| format | Article |
| id | doaj.art-f2181d410fa041a4a77b734fc56df244 |
| institution | Directory Open Access Journal |
| issn | 2227-9059 |
| language | English |
| last_indexed | 2024-03-10T00:39:48Z |
| publishDate | 2022-09-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Biomedicines |
| spelling | doaj.art-f2181d410fa041a4a77b734fc56df2442023-11-23T15:10:36ZengMDPI AGBiomedicines2227-90592022-09-01109218810.3390/biomedicines10092188An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic ReviewMikołaj Błaziak0Szymon Urban1Weronika Wietrzyk2Maksym Jura3Gracjan Iwanek4Bartłomiej Stańczykiewicz5Wiktor Kuliczkowski6Robert Zymliński7Maciej Pondel8Petr Berka9Dariusz Danel10Jan Biegus11Agnieszka Siennicka12Institute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandInstitute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandInstitute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandDepartment of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, PolandInstitute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandDepartment of Psychiatry, Division of Consultation Psychiatry and Neuroscience, Wroclaw Medical University, 50-367 Wroclaw, PolandInstitute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandInstitute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandInstitute of Information Systems in Economics, Wroclaw University of Economics and Business, 53-345 Wroclaw, Poland Department of Information and Knowledge Engineering, Prague University of Economics and Business, W. Churchill Sq. 1938/4, 130 67 Prague, Czech RepublicDepartment of Anthropology, Ludwik Hirszfeld Institute of Immunology and Experimental Therapy, Polish Academy of Sciences, 53-114 Wroclaw, PolandInstitute of Heart Diseases, Wroclaw Medical University, 50-556 Wroclaw, PolandDepartment of Physiology and Pathophysiology, Wroclaw Medical University, 50-368 Wroclaw, PolandHeart 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.https://www.mdpi.com/2227-9059/10/9/2188artificial intelligencemachine learningdeep learningheart failurepredictive modelsystematic review |
| spellingShingle | Mikołaj Błaziak Szymon Urban Weronika Wietrzyk Maksym Jura Gracjan Iwanek Bartłomiej Stańczykiewicz Wiktor Kuliczkowski Robert Zymliński Maciej Pondel Petr Berka Dariusz Danel Jan Biegus Agnieszka Siennicka An Artificial Intelligence Approach to Guiding the Management of Heart Failure Patients Using Predictive Models: A Systematic Review Biomedicines artificial intelligence machine learning deep learning heart failure predictive model 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 | artificial intelligence machine learning deep learning heart failure predictive model systematic review |
| url | https://www.mdpi.com/2227-9059/10/9/2188 |
| work_keys_str_mv | AT mikołajbłaziak anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT szymonurban anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT weronikawietrzyk anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT maksymjura anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT gracjaniwanek anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT bartłomiejstanczykiewicz anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT wiktorkuliczkowski anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT robertzymlinski anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT maciejpondel anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT petrberka anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT dariuszdanel anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT janbiegus anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT agnieszkasiennicka anartificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT mikołajbłaziak artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT szymonurban artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT weronikawietrzyk artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT maksymjura artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT gracjaniwanek artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT bartłomiejstanczykiewicz artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT wiktorkuliczkowski artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT robertzymlinski artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT maciejpondel artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT petrberka artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT dariuszdanel artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT janbiegus artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview AT agnieszkasiennicka artificialintelligenceapproachtoguidingthemanagementofheartfailurepatientsusingpredictivemodelsasystematicreview |