A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome.
Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (MASLD), atherogenic dyslipidaemia (ATD), and chronic kidney disease (CKD). The absence of reliable tools for early diagnosis and risk stratification l...
| Publicado en: | European Journal of Public Health Vol. 35; no. 4; pp. 642 - 650 |
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| Autores principales: | , , , , , , , , |
| Formato: | research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
Aug2025
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| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=187125749&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 187125749 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 11011262 BHW jtl: European Journal of Public Health issn: 11011262 maglogo: N pubinfo: dt: Aug2025 vid: 35 iid: 4 pid: 622 pub: Oxford University Press / USA artinfo: ui: 187125749 187125749 187125749 10.1093/eurpub/ckaf098 187125749 ppf: 642 ppct: 8 formats: fmt: – @attributes: type: T – @attributes: type: P tig: atl: A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome. aug: au: Clarós, Alejandro Ciudin, Andreea Muria, Jordi Llull, Lluis Mola, Jose Àngel Pons, Martí Castán, Javier Cruz, Juan Carlos Simó, Rafael affil: Higia.ai, Barcelona, Spain sug: subj: Metabolic Syndrome X Complications Noncommunicable Diseases Risk Factors Noncommunicable Diseases Prevention and Control Metabolic Syndrome X Risk Factors Patient Centered Care Artificial Intelligence Prediction Models Machine Learning Human Electronic Health Records Office Visits Comparative Studies Diagnosis, Delayed Treatment Delay Descriptive Statistics Hospitalization Workflow Public Health ab: Metabolic syndrome (MetS) is related to non-communicable diseases (NCDs) such as type 2 diabetes (T2D), metabolic-associated steatotic liver disease (MASLD), atherogenic dyslipidaemia (ATD), and chronic kidney disease (CKD). The absence of reliable tools for early diagnosis and risk stratification leads to delayed detection, preventable hospitalizations, and increased healthcare costs. This study evaluates the impact of Transformer-based artificial intelligence (AI) model in predicting and managing MetS-related NCDs compared to classical machine learning models. Electronical medical data registered in the MIMIC-IV v2.2database from 183 958 patients with at least two recorded medical visits were analysed. A two-stage AI approach was implemented: (1) pretraining on 60% of the dataset to capture disease progression patterns, and (2) fine-tuning on the remaining 40% for disease-specific predictions. Transformer-based models was compared with traditional machine learning approaches (Random Forest and Linear Support Vector Classifier [SVC]), evaluating predictive performance through AUC and F1-score. The Transformer-based model significantly outperformed classical models, achieving higher AUC values across all diseases. It also identified a substantial number of undiagnosed cases compared to documented diagnoses fold increase for CKD 2.58, T2D 0.78, dyslipidaemia 1.89, hypertension 3.33, MASLD 5.78, and obesity 4.07. Diagnosis delays ranged from 90 to 500 days, with 35% of missed intervention opportunities occurring within the first five appointments. These delays correlated with an 84% increase in hospitalizations and a 69% rise in medical procedures. This study demonstrates that Transformer-based AI models offer superior predictive accuracy over traditional methods by capturing complex temporal disease patterns. Their integration into clinical workflows and public health strategies could enable scalable, proactive MetS management, reducing undiagnosed cases, optimizing resource allocation, and improving population health outcomes. pubtype: Academic Journal doctype: research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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