{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T04:23:34Z","timestamp":1772252614870,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T00:00:00Z","timestamp":1624233600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Bioengineering"],"abstract":"<jats:p>Artificial Intelligence is creating a paradigm shift in health care, with phenotyping patients through clustering techniques being one of the areas of interest. Objective: To develop a predictive model to classify heart failure (HF) patients according to their left ventricular ejection fraction (LVEF), by using available data from Electronic Health Records (EHR). Subjects and methods: 2854 subjects over 25 years old with a diagnosis of HF and LVEF, measured by echocardiography, were selected to develop an algorithm to predict patients with reduced EF using supervised analysis. The performance of the developed algorithm was tested in heart failure patients from Primary Care. To select the most influentual variables, the LASSO algorithm setting was used, and to tackle the issue of one class exceeding the other one by a large amount, we used the Synthetic Minority Oversampling Technique (SMOTE). Finally, Random Forest (RF) and XGBoost models were constructed. Results: The full XGBoost model obtained the maximum accuracy, a high negative predictive value, and the highest positive predictive value. Gender, age, unstable angina, atrial fibrillation and acute myocardial infarct are the variables that most influence EF value. Applied in the EHR dataset, with a total of 25,594 patients with an ICD-code of HF and no regular follow-up in cardiology clinics, 6170 (21.1%) were identified as pertaining to the reduced EF group. Conclusion: The obtained algorithm was able to identify a number of HF patients with reduced ejection fraction, who could benefit from a protocol with a strong possibility of success. Furthermore, the methodology can be used for studies using data extracted from the Electronic Health Records.<\/jats:p>","DOI":"10.3390\/bioengineering8060085","type":"journal-article","created":{"date-parts":[[2021,6,21]],"date-time":"2021-06-21T13:29:58Z","timestamp":1624282198000},"page":"85","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":2,"title":["Supervised Analysis for Phenotype Identification: The Case of Heart Failure Ejection Fraction Class"],"prefix":"10.3390","volume":"8","author":[{"given":"Cristina","family":"Lopez","sequence":"first","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose Luis","family":"Holgado","sequence":"additional","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-9607-997X","authenticated-orcid":false,"given":"Raquel","family":"Cortes","sequence":"additional","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Inma","family":"Sauri","sequence":"additional","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3755-4587","authenticated-orcid":false,"given":"Antonio","family":"Fernandez","sequence":"additional","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Jose Miguel","family":"Calderon","sequence":"additional","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1672-7119","authenticated-orcid":false,"given":"Julio","family":"Nu\u00f1ez","sequence":"additional","affiliation":[{"name":"Cardiology Hospital Cl\u00ednico of Valencia, 46010 Valencia, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8777-6773","authenticated-orcid":false,"given":"Josep","family":"Redon","sequence":"additional","affiliation":[{"name":"Cardiovascular and Renal Research Group, INCLIVA Research Institute, University of Valencia, 46010 Valencia, Spain"},{"name":"Internal Medicine Hospital Cl\u00ednico of Valencia, 46010 Valencia, Spain"},{"name":"CIBERObn, Carlos III Health Institute, 28029 Madrid, Spain"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2021,6,21]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/j.ccm.2018.10.016","article-title":"Three Major Efforts to Phenotype Asthma: Severe Asthma Research Program, Asthma Disease Endotyping for Personalized Therapeutics, and Unbiased Biomarkers for the Prediction of Respiratory Disease Outcome","volume":"40","author":"Silkoff","year":"2019","journal-title":"Clin. 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