{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,30]],"date-time":"2026-06-30T17:59:31Z","timestamp":1782842371590,"version":"3.54.5"},"reference-count":46,"publisher":"JMIR Publications Inc.","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["JMIR Public Health Surveill"],"abstract":"\n Background<\/jats:title>\n Multimorbidity is a significant public health concern, characterized by the coexistence and interaction of multiple preexisting medical conditions. This complex condition has been associated with an increased risk of COVID-19. Individuals with multimorbidity who contract COVID-19 often face a significant reduction in life expectancy. The postpandemic period has also highlighted an increase in frailty, emphasizing the importance of integrating existing multimorbidity details into epidemiological risk assessments. Managing clinical data that include medical histories presents significant challenges, particularly due to the sparsity of data arising from the rarity of multimorbidity conditions. Also, the complex enumeration of combinatorial multimorbidity features introduces challenges associated with combinatorial explosions.<\/jats:p>\n <\/jats:sec>\n \n Objective<\/jats:title>\n This study aims to assess the severity of COVID-19 in individuals with multiple medical conditions, considering their demographic characteristics such as age and sex. We propose an evolutionary machine learning model designed to handle sparsity, analyzing preexisting multimorbidity profiles of patients hospitalized with COVID-19 based on their medical history. Our objective is to identify the optimal set of multimorbidity feature combinations strongly associated with COVID-19 severity. We also apply the Apriori algorithm to these evolutionarily derived predictive feature combinations to identify those with high support.<\/jats:p>\n <\/jats:sec>\n \n Methods<\/jats:title>\n We used data from 3 administrative sources in Piedmont, Italy, involving 12,793 individuals aged 45-74 years who tested positive for COVID-19 between February and May 2020. From their 5-year pre\u2013COVID-19 medical histories, we extracted multimorbidity features, including drug prescriptions, disease diagnoses, sex, and age. Focusing on COVID-19 hospitalization, we segmented the data into 4 cohorts based on age and sex. Addressing data imbalance through random resampling, we compared various machine learning algorithms to identify the optimal classification model for our evolutionary approach. Using 5-fold cross-validation, we evaluated each model\u2019s performance. Our evolutionary algorithm, utilizing a deep learning classifier, generated prediction-based fitness scores to pinpoint multimorbidity combinations associated with COVID-19 hospitalization risk. Eventually, the Apriori algorithm was applied to identify frequent combinations with high support.<\/jats:p>\n <\/jats:sec>\n \n Results<\/jats:title>\n We identified multimorbidity predictors associated with COVID-19 hospitalization, indicating more severe COVID-19 outcomes. Frequently occurring morbidity features in the final evolved combinations were age>53, R03BA (glucocorticoid inhalants), and N03AX (other antiepileptics) in cohort 1; A10BA (biguanide or metformin) and N02BE (anilides) in cohort 2; N02AX (other opioids) and M04AA (preparations inhibiting uric acid production) in cohort 3; and G04CA (Alpha-adrenoreceptor antagonists) in cohort 4.<\/jats:p>\n <\/jats:sec>\n \n Conclusions<\/jats:title>\n When combined with other multimorbidity features, even less prevalent medical conditions show associations with the outcome. This study provides insights beyond COVID-19, demonstrating how repurposed administrative data can be adapted and contribute to enhanced risk assessment for vulnerable populations.<\/jats:p>\n <\/jats:sec>","DOI":"10.2196\/52353","type":"journal-article","created":{"date-parts":[[2024,7,18]],"date-time":"2024-07-18T11:00:34Z","timestamp":1721300434000},"page":"e52353","source":"Crossref","is-referenced-by-count":2,"title":["A Multimorbidity Analysis of Hospitalized Patients With COVID-19 in Northwest Italy: Longitudinal Study Using Evolutionary Machine Learning and Health Administrative Data"],"prefix":"10.2196","volume":"10","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-2503-3741","authenticated-orcid":false,"given":"Dayana","family":"Benny","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7647-5649","authenticated-orcid":false,"given":"Mario","family":"Giacobini","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2597-2060","authenticated-orcid":false,"given":"Alberto","family":"Catalano","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-3187-4889","authenticated-orcid":false,"given":"Giuseppe","family":"Costa","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1919-2199","authenticated-orcid":false,"given":"Roberto","family":"Gnavi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8749-9737","authenticated-orcid":false,"given":"Fulvio","family":"Ricceri","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1010","published-online":{"date-parts":[[2024,7,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","DOI":"10.1038\/nrrheum.2013.212"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0169426"},{"key":"ref3","doi-asserted-by":"publisher","DOI":"10.1145\/3097983.3098109"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1016\/S2214-109X(20)30264-3"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.2196\/41404"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1136\/bmj.m160"},{"key":"ref7","doi-asserted-by":"publisher","DOI":"10.1136\/bmjopen-2016-013089"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclinepi.2004.03.012"},{"key":"ref9","doi-asserted-by":"publisher","DOI":"10.26633\/RPSP.2020.166"},{"key":"ref10","doi-asserted-by":"publisher","DOI":"10.1186\/1471-2458-11-101"},{"key":"ref11","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pmed.1002501"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1016\/j.lanepe.2021.100047"},{"key":"ref13","doi-asserted-by":"publisher","DOI":"10.1038\/s41598-019-51135-7"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1371\/journal.pone.0032190"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1186\/1472-6963-13-219"},{"key":"ref16","doi-asserted-by":"publisher","DOI":"10.1093\/bib\/bbac255"},{"key":"ref17","doi-asserted-by":"publisher","DOI":"10.3390\/data6080085"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.11648\/j.mlr"},{"key":"ref19","doi-asserted-by":"publisher","DOI":"10.1186\/s12874-024-02200-x"},{"key":"ref20","doi-asserted-by":"publisher","DOI":"10.1109\/IRASET57153.2023.10153014"},{"key":"ref21","doi-asserted-by":"publisher","DOI":"10.1007\/s12065-019-00218-5"},{"key":"ref22","doi-asserted-by":"publisher","DOI":"10.1109\/iccni.2017.8123815"},{"key":"ref23","doi-asserted-by":"publisher","DOI":"10.1145\/3449726.3463174"},{"key":"ref24","doi-asserted-by":"publisher","DOI":"10.3390\/ijerph192416756"},{"key":"ref25","doi-asserted-by":"publisher","DOI":"10.1007\/s13042-010-0004-x"},{"key":"ref26","doi-asserted-by":"publisher","DOI":"10.1016\/j.artmed.2023.102659"},{"key":"ref27","doi-asserted-by":"publisher","DOI":"10.5555\/3295222.3295230"},{"key":"ref28","unstructured":"Established Legislative Decree no. 322\/1989 concerning National Statistical System organization the Sistan includes: the National Institute of Statistics (ISTAT); 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