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Updated: Jul 19, 2026

Asthma Detection Research Based on Voice Signal Processing and Machine Learning
Published on: July 22, 2025
Computer-based decision support for pediatric asthma management: description and feasibility of the Stop Asthma
Ross Shegog1, Leona K Bartholomew, Marianna M Sockrider
1Center for Health Promotion and Prevention Research, UT-School of Public Health, 7000 Fannin, Suite 2668, Houston, TX 77030, USA. Ross.Shegog@uth.tmc.edu
Abstract:
Clinical guidelines can assist in the management of asthma. Decision support systems (DSSs) can enhance adherence to clinical guidelines but tend not to provide clinicians with cues for behavioral change strategies to promote patient self-management. The Stop Asthma Clinical System (SACS) is a DSS designed for this purpose. To assess feasibility, seven clinicians used SACS to guide well visits with 26 predominantly persistent pediatric asthma patients. Data were collected via survey and in-depth semi-structured interviews. SACS improved assessment of asthma severity and control, classification of and intervention in medicine and environmental trigger management problems, and development of an action plan (all p < 0.05). Clinician-patient communication was enhanced. The primary challenge was that SACS increased clinic visit time. SACS can enhance clinician behavior to improve patient asthma self-management, but more studies are indicated to mitigate temporal constraints and evaluate impact on clinician and patient communication and behavior as well as clinical outcomes.
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