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Characterizing the clinical profile and prevalence of people with diabetes attended in the hospital setting by using unstructured healthcare data and natural language processing: the Diabetic@ study.

Fecha de publicación: Fecha Ahead of Print:

Autores de IIS La Fe

Participantes ajenos a IIS La Fe

  • Blanco-Carrasco AJ
  • Canovas Molina G
  • Brito-Sanfiel MA
  • Barajas Galindo DE
  • Cuellar Olmedo LA
  • Mauricio D
  • Tofé Povedano S
  • Balsa Barro JA
  • Aparicio Sánchez JJ
  • Sequera Mutiozabal M
  • Pimentel B
  • Pérez Domínguez A
  • Arias-Cabrales C
  • Fanjul V
  • de Isla LP
  • Navarro González JF

Grupos

Abstract

AIMS: This study aimed to evaluate the potential of unstructured electronic health records (EHRs) data, analyzed using natural language processing (NLP) and machine learning (ML), to describe the prevalence and clinical spectrum of diabetes mellitus (DM) in hospitals. METHODS: A multicenter, retrospective study was conducted using EHRs from eight Spanish hospitals (2013-2018). Unstructured data were extracted using EHRead® (NLP and ML) and SNOMED_CT. Individuals with type 1 or 2 DM (T1DM/T2DM) were identified, and a semi-supervised ML classifier was developed for unregistered types (UrDM). DM prevalence and related complications were analyzed in the final subpopulations (sT1DM/sT2DM). RESULTS: From 56,181,954 EHRs of 2,582,778 individuals, 638,730 were identified with DM: 75.4?% with UrDM, 21.3?% with T2DM, and 3.3?% with T1DM. The ML model reclassified 93.5?% as T2DM and 6.5?% as T1DM. Over 50?% of relevant variables like anthropometrics, lab values and treatments were missing. The prevalence of sT1DM/sT2DM was 2.6?%/38.4?%. Major comorbidities included hypertension, dyslipidemia, chronic kidney disease (CKD), ischemic heart disease, and chronic heart failure (CHF). CKD and CHF were the most frequent complications for sT1DM/sT2DM at 60?months. CONCLUSIONS: NLP and ML for profiling DM using EHRs unstructured data are helpful, but additional data and better EHR documentation are crucial.

Copyright © 2025. Published by Elsevier B.V.

Datos de la publicación

ISSN/ISSNe:
0168-8227, 1872-8227

DIABETES RESEARCH AND CLINICAL PRACTICE  ELSEVIER IRELAND LTD

Tipo:
Article
Páginas:
112214-112214
PubMed:
40319920
Factor de Impacto:
1,677 SCImago ℠
Cuartil:
Q1 SCImago ℠

Citas Recibidas en Web of Science: 4

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Keywords

  • Diabetes Comorbidities; Diabetes Complications; Diabetes Mellitus (DM); Machine Learning (ML); Natural Language Processing (NLP); Unstructured Data

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