ArticleArchives of public health = Archives belges de sante publique2021
Use of artificial intelligence for public health surveillance: a case study to develop a machine Learning-algorithm to estimate the incidence of diabetes mellitus in France.
Article in Archives of public health = Archives belges de sante publique, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 15 papers.
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Who cites it
15 citing papers in PubMed.
- CONSTANCES, the French nationwide population cohort: major accomplishments.European journal of epidemiology · 2026Article
- Artificial Intelligence in Public Health Education: A Scoping Review of Workforce Competency Development.Health science reports · 2026Article
- Identifying diseases in claims data using a machine learning approach - a case from Switzerland.Archives of public health = Archives belges de sante publique · 2025Article
- Personalized fitness recommendations using machine learning for optimized national health strategy.Scientific reports · 2025Article
- Analysis and Mapping of Machine Learning in the Context of Diabetes.Health science reports · 2025Article
- Artificial Intelligence Enabled Lifestyle Medicine in Diabetes Care: A Narrative Review.American journal of lifestyle medicine · 2025Review
- Development and indirect validation of a model predicting frailty in the French healthcare claims database.Scientific reports · 2025Article
- Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.PloS one · 2025Article
- Exploring machine learning classification for community based health insurance enrollment in Ethiopia.Frontiers in public health · 2025Article
- Bias in machine learning applications to address non-communicable diseases at a population-level: a scoping review.BMC public health · 2024Article
- Unsupervised algorithms to identify potential under-coding of secondary diagnoses in hospitalisations databases in Portugal.Health information management : journal of the Health Information Management Association of Australia · 2024Article
- Predicting dyslipidemia incidence: unleashing machine learning algorithms on Lifestyle Promotion Project data.BMC public health · 2024Article
- Enhancement of Classifier Performance Using Swarm Intelligence in Detection of Diabetes from Pancreatic Microarray Gene Data.Biomimetics (Basel, Switzerland) · 2023Article
- Predicting Multimorbidity Using Saudi Health Indicators (Sharik) Nationwide Data: Statistical and Machine Learning Approach.Healthcare (Basel, Switzerland) · 2023Article
- Machine learning and deep learning predictive models for type 2 diabetes: a systematic review.Diabetology & metabolic syndrome · 2021Review
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Authors and funding
7 authors.
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Abstract
backgroundThe use of machine learning techniques is increasing in healthcare which allows to estimate and predict health outcomes from large administrative data sets more efficiently. The main objective of this study was to develop a generic machine learning (ML) algorithm to estimate the incidence of diabetes based on the number of reimbursements over the last 2 years.
methodsWe selected a final data set from a population-based epidemiological cohort (i.e., CONSTANCES) linked with French National Health Database (i.e., SNDS). To develop this algorithm, we adopted a supervised ML approach. Following steps were performed: i. selection of final data set, ii. target definition, iii. Coding variables for a given window of time, iv. split final data into training and test data sets, v. variables selection, vi. training model, vii. Validation of model with test data set and viii. Selection of the model. We used the area under the receiver operating characteristic curve (AUC) to select the best algorithm.
resultsThe final data set used to develop the algorithm included 44,659 participants from CONSTANCES. Out of 3468 variables from SNDS linked to CONSTANCES cohort were coded, 23 variables were selected to train different algorithms. The final algorithm to estimate the incidence of diabetes was a Linear Discriminant Analysis model based on number of reimbursements of selected variables related to biological tests, drugs, medical acts and hospitalization without a procedure over the last 2 years. This algorithm has a sensitivity of 62%, a specificity of 67% and an accuracy of 67% [95% CI: 0.66-0.68].
conclusionsSupervised ML is an innovative tool for the development of new methods to exploit large health administrative databases. In context of InfAct project, we have developed and applied the first time a generic ML-algorithm to estimate the incidence of diabetes for public health surveillance. The ML-algorithm we have developed, has a moderate performance. The next step is to apply this algorithm on SNDS to estimate the incidence of type 2 diabetes cases. More research is needed to apply various MLTs to estimate the incidence of various health conditions.
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