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Predicting diabetes mellitus metabolic goals and chronic complications transitions—analysis based on natural language processing and machine learning models

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dc.creatorLochmuller, Christianspa
dc.creatorRodríguez Lesmes, Paul Andrésspa
dc.creatorColmenares-Mejia, Claudia C.spa
dc.creatorGarcía Suaza, Andrés Felipespa
dc.creatorIsaza-Ruget, Mario A.spa
dc.creatorMorales-Mendoza, Estebanspa
dc.creatorCéspedes, Yohan R.spa
dc.creatorRincón, Julianaspa
dc.creatorMartínez, Juan P.spa
dc.creatorCamacho-Cogollo, J.E.spa
dc.creatorAtehortúa, Sara C.spa
dc.date.accessioned2025-07-21T16:51:52Z
dc.date.available2025-07-21T16:51:52Z
dc.date.created2025-04-15spa
dc.date.issued2025-04-15spa
dc.description.abstractObjective To estimate Diabetes mellitus (DM) progression at one and two years in terms of glycemic targets and development of complications. Research design and methods We analyzed a retrospective cohort of adult DM patients treated in a Health Maintenance Organization in Colombia, including those with at least one glycosylated hemoglobin (HbA1c) measurement in 2018, 2019, and 2020. We defined four disease transition stages based on metabolic goals according to HbA1c levels and complications: 1. Within HbA1c goals and without complications; 2. Outside goals and without complications, 3. Within goals, but with complications, and 4. Outside goals and with complications. We applied Natural Language Processing (NLP) techniques to extract relevant clinical information from Electronic Health Records. Machine learning (ML) models were used to predict patient progression. Results A total of 23,802 patients were included. Despite achieving initial glycemic control, more than 60% of patients who started within HbA1c targets and without complications developed chronic complications within two years. Our models, which achieved up to 80% accuracy and F1 scores above 74%, identified key predictors of disease progression. Adherence to dyslipidemia treatment guidelines significantly reduced the likelihood of HbA1c deterioration and complications, whereas non-adherence to pharmacological treatments increased the risk of complications. These findings suggest that HbA1c control alone is insufficient to prevent disease progression and that a more comprehensive management approach—including lipid control, kidney function monitoring, and improved adherence to clinical guidelines—is necessary. Conclusions Patient compliance with pharmacological treatments, professional adherence to clinical practice guidelines, and lifestyle interventions play a crucial role in diabetes progression. While our models provide strong predictive capabilities, improving data quality and integration remains essential for better forecasting and intervention strategies.eng
dc.format.mimetypeapplication/pdfspa
dc.identifier.doihttps://doi.org/10.1371/journal.pone.0321258spa
dc.identifier.issn1932-6203spa
dc.identifier.urihttps://repository.urosario.edu.co/handle/10336/46098
dc.language.isoengspa
dc.publisherPlos Onespa
dc.relation.ispartofDiabetes progression with machine learning April 15, 2025spa
dc.relation.urihttps://journals.plos.org/plosone/article?id=10.1371/journal.pone.0321258spa
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internationalspa
dc.rights.accesRightsinfo:eu-repo/semantics/openAccessspa
dc.rights.accesoAbierto (Texto Completo)spa
dc.rights.urihttp://creativecommons.org/licenses/by-nc-sa/4.0/spa
dc.sourceDiabetes progression with machine learningspa
dc.source.instnameinstname:Universidad del Rosariospa
dc.source.reponamereponame:Repositorio Institucional EdocURspa
dc.subject.keywordDiabetes Mellituseng
dc.subject.keywordDM Progressioneng
dc.subject.keywordGlycemic Targetseng
dc.subject.keywordComplicationseng
dc.subject.keywordRetrospective Cohorteng
dc.subject.keywordAdult DM Patientseng
dc.subject.keywordHealth Maintenance Organizationeng
dc.subject.keywordColombiaeng
dc.subject.keywordGlycosylated Hemoglobineng
dc.subject.keywordHbA1ceng
dc.subject.keywordDisease Transition Stageseng
dc.subject.keywordMetabolic Goalseng
dc.subject.keywordNatural Language Processingeng
dc.subject.keywordNLPeng
dc.subject.keywordElectronic Health Recordseng
dc.subject.keywordMachine Learningeng
dc.subject.keywordML Modelseng
dc.subject.keywordPatient Progressioneng
dc.subject.keywordGlycemic Controleng
dc.subject.keywordChronic Complicationseng
dc.subject.keywordAccuracyeng
dc.subject.keywordF1 Scoreseng
dc.subject.keywordDisease Progression Predictorseng
dc.subject.keywordDyslipidemia Treatmenteng
dc.subject.keywordPharmacological Treatmentseng
dc.subject.keywordLipid Controleng
dc.subject.keywordKidney Function Monitoringeng
dc.subject.keywordClinical Guidelineseng
dc.subject.keywordPatient Complianceeng
dc.subject.keywordLifestyle Interventionseng
dc.subject.keywordData Qualityeng
dc.subject.keywordData Integrationeng
dc.subject.keywordForecastingeng
dc.subject.keywordIntervention Strategieseng
dc.titlePredicting diabetes mellitus metabolic goals and chronic complications transitions—analysis based on natural language processing and machine learning modelsspa
dc.typearticlespa
dc.type.hasVersioninfo:eu-repo/semantics/publishedVersionspa
dc.type.spaArtículo de Investigaciónspa
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