{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T06:09:07Z","timestamp":1789711747734,"version":"4.0.1"},"reference-count":32,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T00:00:00Z","timestamp":1789689600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Cardiovasc. Med."],"abstract":"<jats:p>This research (retrospective cohort study) investigates how tailored discharge instructions for heart failure (HF) patients with multiple health conditions can lower their emergency or urgent care readmission risk. Utilizing an AI\/ML approach, this study develops a model that classifies HF patients' readmission risk at discharge by evaluating approximately 20 parameters across five categories, including comorbidities (e.g., liver disease, diabetes), patient demographics (e.g., age, BMI), clinical data (e.g., lipid and blood levels), diagnostic parameters (e.g., ejection fraction, HF type), discharge details (e.g., discharge destination and location), and medication history. The AI model identifies high-risk patients, who are then prescribed a more focused wellness and rehabilitation plan, including in-person visits and regular monitoring. Lower-risk patients are given less rigorous plans, involving virtual check-ins and self-management tools. In contrast, high-risk patients can undergo intensive wellness plans, involving hospital\/clinical visits and tracking by wellness staff. The study's approach of customizing post-discharge care based on re-admission risk aims to reduce re-admissions and improve HF patient outcomes. This research employs key ML models on the PhysioNet dataset, a collection of hospital admissions and mortality records for HF patients from Zigong Fourth People's Hospital in Sichuan, China (2016-2019). To evaluate the broader applicability of the developed models, their results on the PhysioNet dataset are benchmarked against the HCUP Maryland SID (2014), a secondary dataset of HF patients. The ML models exhibited strong performance, achieving efficiencies exceeding 76% and specificities reaching 97% in identifying HF patients with a higher risk of readmission and ranking them according to their risk level. The study's approach to customizing post-discharge care based on readmission risk aims to reduce readmissions and improve HF patient outcomes. Thus, this presents a comprehensive and reliable strategy for HF management and wellness.<\/jats:p>","DOI":"10.3389\/fcvm.2026.1612592","type":"journal-article","created":{"date-parts":[[2026,9,18]],"date-time":"2026-09-18T05:39:35Z","timestamp":1789709975000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["A machine learning model for predicting six-month rehospitalization in heart failure patients with multiple comorbidities"],"prefix":"10.3389","volume":"13","author":[{"given":"Rahul","family":"Awasthy","sequence":"first","affiliation":[{"name":"Data Science, Harrisburg University of Science and Technology","place":["Harrisburg, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Michael L.","family":"Seavers","sequence":"additional","affiliation":[{"name":"Data Science, Harrisburg University of Science and Technology","place":["Harrisburg, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Aayush","family":"Awasthy","sequence":"additional","affiliation":[{"name":"Data Science, Harrisburg University of Science and Technology","place":["Harrisburg, PA, United States"]}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1965","published-online":{"date-parts":[[2026,9,18]]},"reference":[{"key":"B1","doi-asserted-by":"publisher","first-page":"808","DOI":"10.1177\/2047487319833512","article-title":"Impact of in-hospital cardiac rehabilitation on mortality and re-admissions in heart failure: a population study in Lombardy, Italy, from 2005 to 2012","volume":"26","author":"Scalvini","year":"2019","journal-title":"Eur J Prev Cardiol"},{"key":"B2","doi-asserted-by":"publisher","DOI":"10.1177\/0046958019860386","article-title":"Re-admission rates and their impact on hospital financial performance: a study of Washington hospitals","volume":"56","author":"Upadhyay","year":"2019","journal-title":"INQUIRY: The Journal of Health Care Organization, Provision, and Financing"},{"key":"B3","doi-asserted-by":"publisher","first-page":"1379336","DOI":"10.3389\/fdgth.2024.1379336","article-title":"Admission prioritization of heart failure patients with multiple comorbidities","volume":"6","author":"Awasthy","year":"2024","journal-title":"Front Digit Health"},{"key":"B4","doi-asserted-by":"publisher","first-page":"259","DOI":"10.1016\/j.pcad.2017.07.004","article-title":"Implementation of a patient navigator program to reduce 30-day heart failure Re-admission rate","volume":"60","author":"Di Palo","year":"2017","journal-title":"Prog Cardiovasc Dis"},{"key":"B5","doi-asserted-by":"publisher","first-page":"444","DOI":"10.1161\/CIRCOUTCOMES.111.000101","article-title":"Hospital strategies associated with 30-day re-admission rates for patients with heart failure","volume":"6","author":"Bradley","year":"2013","journal-title":"Circ Cardiovasc Qual Outcomes"},{"key":"B6","doi-asserted-by":"publisher","first-page":"30","DOI":"10.1007\/s10916-017-0883-4","article-title":"Based real time remote health monitoring systems: a review on patients prioritization and related \u201cBig Data\u201d using body sensors information and communication technology","volume":"42","author":"Kalid","year":"2018","journal-title":"J Med Syst"},{"key":"B7","doi-asserted-by":"publisher","first-page":"46","DOI":"10.1038\/s41597-021-00835-9","article-title":"Electronic healthcare records and external outcome data for hospitalized patients with heart failure","volume":"8","author":"Zhang","year":"2021","journal-title":"Sci Data"},{"key":"B8","doi-asserted-by":"publisher","DOI":"10.13026\/5m60-vs44","article-title":"Hospitalized patients with heart failure: integrating electronic healthcare records and external outcome data (version 1.3)","author":"Zhang","year":"2022","journal-title":"PhysioNet"},{"key":"B9","author":"Sheng","year":""},{"key":"B10","doi-asserted-by":"publisher","first-page":"1315","DOI":"10.1002\/ejhf.1234","article-title":"Incremental benefit of drug therapies for chronic heart failure with reduced ejection fraction: a network meta-analysis","volume":"20","author":"Komajda","year":"2018","journal-title":"Eur J Heart Fail"},{"key":"B11","doi-asserted-by":"publisher","first-page":"140","DOI":"10.1016\/j.annemergmed.2018.09.022","article-title":"Triage performance in emergency medicine: a systematic review","volume":"74","author":"Hinson","year":"2019","journal-title":"Ann Emerg Med"},{"key":"B12","doi-asserted-by":"publisher","first-page":"146","DOI":"10.1016\/j.ijcard.2018.05.015","article-title":"Secular trends in first-time hospitalization for heart failure with the following one-year re-admission and mortality rates in the 3.8 million adult population of Silesia, Poland, between 2010 and 2016. 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