Developing an ML pipeline for asthma and COPD: The case of a Dutch primary care service. Issue 11 (21st July 2021)

• Record Type: Journal Article
• Authors: Mariani, Stefano; Metting, Esther; Lahr, Maarten M. H.; Vargiu, Eloisa; Zambonelli, Franco
• Is Part Of: International journal of intelligent systems. Volume 36:Issue 11(2021)
• Page Start: 6763
• Page End: 6790
• Publication Date: 2021-07-21
• Subjects: asthma -- COPD -- diagnosis -- machine learning -- prediction -- primary care -- treatment; Artificial intelligence -- Periodicals; Expert systems (Computer science) -- Periodicals; Intelligence artificielle -- Périodiques; Systèmes experts (Informatique) -- Périodiques; 006.3
• DOI: 10.1002/int.22568
• Languages: English
• ISSNs: 0884-8173

Abstract

Abstract: A complex combination of clinical, demographic and lifestyle parameters determines the correct diagnosis and the most effective treatment for asthma and Chronic Obstructive Pulmonary Disease patients. Artificial Intelligence techniques help clinicians in devising the correct diagnosis and designing the most suitable clinical pathway accordingly, tailored to the specific patient conditions. In the case of machine learning (ML) approaches, availability of real‐world patient clinical data to train and evaluate the ML pipeline deputed to assist clinicians in their daily practice is crucial. However, it is common practice to exploit either synthetic data sets or heavily preprocessed collections cleaning and merging different data sources. In this paper, we describe an automated ML pipeline designed for a real‐world data set including patients from a Dutch primary care service, and provide a performance comparison of different prediction models for (i) assessing various clinical parameters, (ii) designing interventions, and (iii) defining the diagnosis.