s12890-022-02144-2.pdf 924,16KB
1000 Titel
  • Artificial intelligence to differentiate asthma from COPD in medico-administrative databases
1000 Autor/in
  1. Joumaa, Hassan |
  2. Sigogne, Raphael |
  3. Maravic, Milka |
  4. Perray, Lucas |
  5. Bourdin, Arnaud |
  6. Roche, Nicolas |
1000 Erscheinungsjahr 2022
1000 Publikationstyp
  1. Artikel |
1000 Online veröffentlicht
  • 2022-09-20
1000 Erschienen in
1000 Quellenangabe
  • 357:2022
1000 Copyrightjahr
  • 2022
1000 Lizenz
1000 Verlagsversion
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1000 Ergänzendes Material
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1000 Publikationsstatus
1000 Begutachtungsstatus
1000 Sprache der Publikation
1000 Abstract/Summary
  • INTRODUCTION: Discriminating asthma from chronic obstructive pulmonary disease (COPD) using medico-administrative databases is challenging but necessary for medico-economic analyses focusing on respiratory diseases. Artificial intelligence (AI) may improve dedicated algorithms. OBJECTIVES: To assess performance of different AI-based approaches to distinguish asthmatics from COPD patients in medico-administrative databases where the clinical diagnosis is absent. An “Asthma COPD Overlap” category was defined to further test whether AI can detect complexity. METHODS: This study included 178,962 patients treated by two “R03” treatment prescriptions at least from January 2016 to December 2018 and managed by either a general practitioner and/or a pulmonologist participating in a permanent longitudinal observatory of prescription in ambulatory medicine (LPD). Clinical diagnoses are available in this database and were used as gold standards to develop diagnostic rules. Three types of AI approaches were explored using data restricted to demographics and treatment dispensations: multinomial regression, gradient boosting and recurrent neural networks (RNN). The best performing model (based on metric properties) was then applied to estimate the size of asthma and COPD populations based on a database (LRx) of treatment dispensations between July, 2018 and June, 2019. RESULTS: The best models were obtained with the boosting approach and RNN, with an overall accuracy of 68%. Performance metrics were better for asthma than COPD. Based on LRx data, the extrapolated numbers of patients treated for asthma and COPD in France were 3.7 and 1.2 million, respectively. Asthma patients were younger than COPD patients (mean, 49.9 vs. 72.1 years); COPD occurred mostly in men (68%) compared to asthma (33%). CONCLUSION: AI can provide models with acceptable accuracy to distinguish between asthma, ACO and COPD in medico-administrative databases where the clinical diagnosis is absent. Deep learning and machine learning (RNN) had similar performances in this regard.
1000 Sacherschließung
lokal Epidemiology
lokal Prevalence
lokal COPD
lokal Algorithms
lokal Chronic obstructive pulmonary disease
lokal Asthma
lokal Healthcare administrative databases
lokal ICD code
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