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Title:           A model based on artificial intelligence for the prediction, prevention and patient-centred approach for non-communicable diseases related to metabolic syndrome
Subject:         Doi: 10.1093/eurpub/ckaf098 European Journal of Public Health, 35, 4, 2025 Publication Date: 03/07/2025 Abstract 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.
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  Checksum: 35887ed9
   Type: CRC32
  Checksum: 12855a7a138e7873a2fc69c2140ab0bb
   Type: MD5
  Checksum: 7409ca872270155661a67bffa7b1b8d038cd4e4e
   Type: SHA-1
  Checksum: 09000f65c7be6410b992c14a9d9a05007aa6ccfe3b6bd879c7cb903076ea303e
   Type: SHA-256