{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,8]],"date-time":"2026-09-08T10:20:13Z","timestamp":1788862813713,"version":"build-2803163510"},"reference-count":30,"publisher":"Oxford University Press (OUP)","issue":"5","license":[{"start":{"date-parts":[[2025,12,20]],"date-time":"2025-12-20T00:00:00Z","timestamp":1766188800000},"content-version":"vor","delay-in-days":1590,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"},{"start":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T00:00:00Z","timestamp":1628812800000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"funder":[{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["19ek0109367h0002"],"award-info":[{"award-number":["19ek0109367h0002"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["20ek0109367h0003"],"award-info":[{"award-number":["20ek0109367h0003"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"KAKENHI","doi-asserted-by":"publisher","award":["19K17529"],"award-info":[{"award-number":["19K17529"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["19ek0109367h0002"],"award-info":[{"award-number":["19ek0109367h0002"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100009619","name":"Japan Agency for Medical Research and Development","doi-asserted-by":"publisher","award":["20ek0109367h0003"],"award-info":[{"award-number":["20ek0109367h0003"]}],"id":[{"id":"10.13039\/100009619","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001691","name":"Japan Society for the Promotion of Science","doi-asserted-by":"publisher","award":["19K17529"],"award-info":[{"award-number":["19K17529"]}],"id":[{"id":"10.13039\/501100001691","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2021,10,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aims<\/jats:title>\n                    <jats:p>Individual risk stratification is a fundamental strategy in managing patients with heart failure (HF). Artificial intelligence, particularly machine learning (ML), can develop superior models for predicting the prognosis of HF patients, and administrative claim data (ACD) are suitable for ML analysis because ACD is a structured database. The objective of this study was to analyse ACD using an ML algorithm, predict the 1 year mortality of patients with HF, and finally develop an easy-to-use prediction model with high accuracy using the top predictors identified by the ML algorithm.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods and results<\/jats:title>\n                    <jats:p>Machine learning-based prognostic prediction models were developed from the ACD on 10 175 HF patients from the Japanese Registry of Acute Decompensated Heart Failure with 17% mortality during 1 year follow-up. The top predictors for prognosis in HF were identified by the permutation feature importance technique, and an easy-to-use prediction model was developed based on these predictors. The c-statistics and Brier scores of the developed ML-based models were compared with those of conventional risk models: Seattle Heart Failure Model (SHFM) and Meta-Analysis Global Group in Chronic Heart Failure (MAGGIC). A voting classifier algorithm (ACD-VC) achieved the highest c-statistics among the six ML algorithms. The permutation feature importance technique enabled identification of the top predictors such as Barthel index, age, body mass index, duration of hospitalization, last hospitalization, renal disease, and non-loop diuretics use (feature importance values were 0.054, 0.025, 0.010, 0.005, 0.005, 0.004, and 0.004, respectively). Upon combination of some of the predictors that can be assessed from a brief interview, the Simple Model by ARTificial intelligence for HF risk stratification (SMART-HF) was established as an easy-to-use prediction model. Compared with the conventional models, SMART-HF achieved a higher c-statistic {ACD-VC: 0.777 [95% confidence interval (CI) 0.751\u20130.803], SMART-HF: 0.765 [95% CI 0.739\u20130.791], SHFM: 0.713 [95% CI 0.684\u20130.742], MAGGIC: 0.726 [95% CI 0.698\u20130.753]} and better Brier scores (ACD-VC: 0.121, SMART-HF: 0.124, SHFM: 0.139, MAGGIC: 0.130).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions<\/jats:title>\n                    <jats:p>The ML model based on ACD predicted the 1 year mortality of HF patients with high accuracy, and SMART-HF along with the ML model achieved superior performance to that of the conventional risk models. The SMART-HF model has the clear merit of easy operability even by non-healthcare providers with a user-friendly online interface (https:\/\/hfriskcalculator.herokuapp.com\/). Risk models developed using SMART-HF may provide a novel modality for risk stratification of patients with HF.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1002\/ehf2.13556","type":"journal-article","created":{"date-parts":[[2021,8,13]],"date-time":"2021-08-13T05:22:18Z","timestamp":1628832138000},"page":"4077-4085","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":47,"title":["Machine Learning-Based Model for Predicting 1 Year Mortality of Hospitalized Patients with Heart Failure"],"prefix":"10.1093","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-7300-8180","authenticated-orcid":false,"given":"Takeshi","family":"Tohyama","sequence":"first","affiliation":[{"name":"Center for Clinical and Translational Research of Kyushu University Hospital , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]},{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Tomomi","family":"Ide","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Masataka","family":"Ikeda","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hidetaka","family":"Kaku","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Nobuyuki","family":"Enzan","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Shouji","family":"Matsushima","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Kouta","family":"Funakoshi","sequence":"additional","affiliation":[{"name":"Center for Clinical and Translational Research of Kyushu University Hospital , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Junji","family":"Kishimoto","sequence":"additional","affiliation":[{"name":"Center for Clinical and Translational Research of Kyushu University Hospital , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Koji","family":"Todaka","sequence":"additional","affiliation":[{"name":"Center for Clinical and Translational Research of Kyushu University Hospital , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hiroyuki","family":"Tsutsui","sequence":"additional","affiliation":[{"name":"Department of Cardiovascular Medicine, Faculty of Medical Sciences, Kyushu University , Higashi-ku, Fukuoka-shi, Fukuoka ,","place":["Japan"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"286","published-online":{"date-parts":[[2021,8,13]]},"reference":[{"key":"2026012114223468900_ehf213556-bib-0001","doi-asserted-by":"crossref","first-page":"7","DOI":"10.15420\/cfr.2016:25:2","article-title":"Global public health burden of heart failure","volume":"03","author":"Savarese","year":"2017","journal-title":"Card Fail Rev."},{"key":"2026012114223468900_ehf213556-bib-0002","first-page":"e28","article-title":"Heart disease and stroke statistics\u20142014 update: a report from the American Heart Association","volume":"129","author":"Go","year":"2014","journal-title":"Circulation"},{"key":"2026012114223468900_ehf213556-bib-0003","doi-asserted-by":"crossref","first-page":"1404","DOI":"10.1093\/eurheartj\/ehs337","article-title":"Predicting survival in heart failure: A risk score based on 39 372 patients from 30 studies","volume":"34","author":"Pocock","year":"2013","journal-title":"Eur Heart J."},{"key":"2026012114223468900_ehf213556-bib-0004","doi-asserted-by":"crossref","first-page":"368","DOI":"10.1016\/j.ijcard.2013.12.028","article-title":"The annual global economic burden of heart failure","volume":"171","author":"Cook","year":"2014","journal-title":"Int J Cardiol."},{"key":"2026012114223468900_ehf213556-bib-0005","doi-asserted-by":"crossref","first-page":"e147","DOI":"10.1016\/j.jacc.2013.05.019","article-title":"2013 ACCF\/AHA guideline for the management of heart failure: A report of the American College of Cardiology Foundation\/American Heart Association Task Force on Practice Guidelines","volume":"62","author":"Yancy","year":"2013","journal-title":"J Am Coll Cardiol."},{"key":"2026012114223468900_ehf213556-bib-0006","first-page":"32087","article-title":"Medicare and Medicaid programs: Hospice conditions of participation. Final rule","volume":"73","author":"Federal","year":"2008","journal-title":"Fed Regist."},{"key":"2026012114223468900_ehf213556-bib-0007","doi-asserted-by":"crossref","first-page":"1424","DOI":"10.1161\/CIRCULATIONAHA.105.584102","article-title":"The Seattle Heart Failure Model: Prediction of survival in heart failure","volume":"113","author":"Levy","year":"2006","journal-title":"Circulation."},{"key":"2026012114223468900_ehf213556-bib-0008","doi-asserted-by":"crossref","first-page":"2823","DOI":"10.1093\/eurheartj\/eht247","article-title":"The impact of eplerenone at different levels of risk in patients with systolic heart failure and mild symptoms: Insight from a novel risk score for prognosis derived from the EMPHASIS-HF trial","volume":"34","author":"Collier","year":"2013","journal-title":"Eur Heart J."},{"key":"2026012114223468900_ehf213556-bib-0009","doi-asserted-by":"crossref","first-page":"435","DOI":"10.1001\/jamacardio.2016.5036","article-title":"Use of risk models to predict death in the next year among individual ambulatory patients with heart failure","volume":"2","author":"Allen","year":"2017","journal-title":"JAMA Cardiol."},{"key":"2026012114223468900_ehf213556-bib-0010","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1161\/CIRCOUTCOMES.116.003039","article-title":"Analysis of machine learning techniques for heart failure readmissions","volume":"9","author":"Mortazavi","year":"2016","journal-title":"Circ Cardiovasc Qual Outcomes."},{"key":"2026012114223468900_ehf213556-bib-0011","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1016\/j.jchf.2019.06.013","article-title":"Machine learning prediction of mortality and hospitalization in heart failure with preserved ejection fraction","volume":"8","author":"Angraal","year":"2020","journal-title":"JACC Heart Fail."},{"key":"2026012114223468900_ehf213556-bib-0012","article-title":"Machine learning versus conventional clinical methods in guiding management of heart failure patients\u2014a systematic review","volume":"1\u20132","author":"Bazoukis","year":"2020","journal-title":"Heart Fail Rev."},{"key":"2026012114223468900_ehf213556-bib-0013","doi-asserted-by":"crossref","DOI":"10.1001\/jamanetworkopen.2019.18962","article-title":"Comparison of machine learning methods with traditional models for use of administrative claims with electronic medical records to predict heart failure outcomes","volume":"3","author":"Desai","year":"2020","journal-title":"JAMA Netw Open."},{"key":"2026012114223468900_ehf213556-bib-0014","doi-asserted-by":"crossref","DOI":"10.1253\/circj.CJ-20-0947","article-title":"Clinical characteristics and outcomes of hospitalized patients with heart failure from the large-scale Japanese Registry Of Acute Decompensated Heart Failure (JROADHF)","author":"Ide","year":"2021","journal-title":"Circ J"},{"key":"2026012114223468900_ehf213556-bib-0015","first-page":"110","article-title":"Medical big data for research use: Current status and related issues","volume":"59","author":"Ishikawa","year":"2016","journal-title":"Japan Med Assoc J."},{"key":"2026012114223468900_ehf213556-bib-0016","doi-asserted-by":"crossref","first-page":"1974","DOI":"10.1097\/CCM.0000000000001828","article-title":"The effect of intraaortic balloon pumping under venoarterial extracorporeal membrane oxygenation on mortality of cardiogenic patients: An analysis using a nationwide inpatient database","volume":"44","author":"Aso","year":"2016","journal-title":"Crit Care Med."},{"key":"2026012114223468900_ehf213556-bib-0017","doi-asserted-by":"crossref","first-page":"1029","DOI":"10.1016\/j.spinee.2012.10.020","article-title":"Factors affecting the occurrence of pulmonary embolism after spinal surgery: Data from the national administrative database in Japan","volume":"12","author":"Masuda","year":"2012","journal-title":"Spine J."},{"key":"2026012114223468900_ehf213556-bib-0018","doi-asserted-by":"crossref","first-page":"373","DOI":"10.1016\/0021-9681(87)90171-8","article-title":"A new method of classifying prognostic comorbidity in longitudinal studies: Development and validation","volume":"40","author":"Charlson","year":"1987","journal-title":"J Chronic Dis."},{"key":"2026012114223468900_ehf213556-bib-0019","doi-asserted-by":"crossref","first-page":"1130","DOI":"10.1097\/01.mlr.0000182534.19832.83","article-title":"Coding algorithms for defining comorbidities in ICD-9-CM and ICD-10 administrative data","volume":"43","author":"Quan","year":"2005","journal-title":"Med Care."},{"key":"2026012114223468900_ehf213556-bib-0020","doi-asserted-by":"crossref","first-page":"519","DOI":"10.1080\/713827181","article-title":"An analysis of four missing data treatment methods for supervised learning","volume":"17","author":"Batista","year":"2003","journal-title":"Appl Artif Intell."},{"key":"2026012114223468900_ehf213556-bib-0021","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","article-title":"Random forests","volume":"45","author":"Breiman","year":"2001","journal-title":"Mach Learn."},{"key":"2026012114223468900_ehf213556-bib-0022","doi-asserted-by":"crossref","first-page":"861","DOI":"10.1016\/j.patrec.2005.10.010","article-title":"An introduction to ROC analysis","volume":"27","author":"Fawcett","year":"2006","journal-title":"Pattern Recognit Lett."},{"key":"2026012114223468900_ehf213556-bib-0023","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1175\/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2","article-title":"Verification of forecasts expressed in terms of probability","volume":"78","author":"Brier","year":"1950","journal-title":"Mon Weather Rev."},{"key":"2026012114223468900_ehf213556-bib-0024","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/1097-0142(1950)3:1<32::AID-CNCR2820030106>3.0.CO;2-3","article-title":"Index for rating diagnostic tests","volume":"3","author":"Youden","year":"1950","journal-title":"Cancer."},{"key":"2026012114223468900_ehf213556-bib-0025","doi-asserted-by":"crossref","first-page":"385","DOI":"10.1080\/095400996116839","article-title":"Error correlation and error reduction in ensemble classifiers","volume":"8","author":"Tumer","year":"1996","journal-title":"Conn Sci."},{"key":"2026012114223468900_ehf213556-bib-0026","doi-asserted-by":"crossref","first-page":"127","DOI":"10.1016\/j.ijcard.2013.12.169","article-title":"Basal functional status predicts three-month mortality after a heart failure hospitalization in elderly patients - The prospective RICA study","volume":"172","author":"Formiga","year":"2014","journal-title":"Int J Cardiol."},{"key":"2026012114223468900_ehf213556-bib-0027","doi-asserted-by":"crossref","first-page":"240","DOI":"10.1016\/j.ijcard.2017.11.010","article-title":"The Barthel index in elderly acute heart failure patients. Frailty matters","volume":"254","author":"Aimo","year":"2018","journal-title":"Int J Cardiol."},{"key":"2026012114223468900_ehf213556-bib-0028","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1371\/journal.pone.0206380","article-title":"Validation of the MAGGIC (Meta-Analysis Global Group In Chronic Heart Failure) heart failure risk score and the effect of adding natriuretic peptide for predicting mortality after discharge in hospitalized patients with heart failure","volume":"13","author":"Khanam","year":"2018","journal-title":"PLoS One."},{"key":"2026012114223468900_ehf213556-bib-0029","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1161\/JAHA.117.008081","article-title":"Machine learning methods improve prognostication, identify clinically distinct phenotypes, and detect heterogeneity in response to therapy in a large cohort of heart failure patients","volume":"7","author":"Ahmad","year":"2018","journal-title":"J Am Heart Assoc."},{"key":"2026012114223468900_ehf213556-bib-0030","doi-asserted-by":"crossref","first-page":"260","DOI":"10.1016\/j.jbi.2016.01.009","article-title":"Developing EHR-driven heart failure risk prediction models using CPXR (Log) with the probabilistic loss function","volume":"60","author":"Taslimitehrani","year":"2016","journal-title":"J Biomed Inform."}],"container-title":["ESC Heart Failure"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/pdf\/10.1002\/ehf2.13556","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/onlinelibrary.wiley.com\/doi\/full-xml\/10.1002\/ehf2.13556","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/academic.oup.com\/eschf\/article-pdf\/8\/5\/4077\/65566282\/eschf_8_5_4077.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"syndication"},{"URL":"https:\/\/academic.oup.com\/eschf\/article-pdf\/8\/5\/4077\/65566282\/eschf_8_5_4077.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,1,21]],"date-time":"2026-01-21T19:28:05Z","timestamp":1769023685000},"score":1,"resource":{"primary":{"URL":"https:\/\/academic.oup.com\/eschf\/article\/8\/5\/4077\/8345851"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2021,8,13]]},"references-count":30,"journal-issue":{"issue":"5","published-online":{"date-parts":[[2021,8,13]]},"published-print":{"date-parts":[[2021,10,1]]}},"URL":"https:\/\/doi.org\/10.1002\/ehf2.13556","relation":{},"ISSN":["2055-5822"],"issn-type":[{"value":"2055-5822","type":"electronic"}],"subject":[],"published-other":{"date-parts":[[2021,10]]},"published":{"date-parts":[[2021,8,13]]},"assertion":[{"value":"2021-02-24","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-07-28","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2021-08-13","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}]}}