Machine Learning Aided Diagnosis Designing and Evaluating Vitas, a Virtual Intelligent Tool for Assessment and Symptom Check, Using the DDXPLUS Dataset - Saet, Myra F.; Landingin, John Vincent N.; Jamir-Domingo, Jeff Louine T.; Lansangan, Joseph Ryan G., PhD. (2024)
ABSTRACT

The healthcare sector in the Philippines is grappling with a critical shortage of medical professionals, resulting in increased workloads and burnout. To address this challenge, we present the Virtual Intelligent Tool for Assessment and Symptom Check (VITAS), a machine learning-based diagnostic tool aimed at enhancing the efficiency and accuracy of patent diagnoses. VITAS employs the DDXPlus dataset and integrates semantic search, adaptive questionnaires, predictive modeling, and model interpretability to optimized the diagnostic process. VITAS utilizes BioBERT for semantic search, accurately mapping patient-reported symptoms to relevant medical evidence. An adaptive questionnaire is designed to balance diagnostic precision with user input time. In performance evaluations, the Random Forest algorithm achieved a misdiagnosis rete of just 1.05% for single diagnose, while the differential diagnosis model recorded a GTPA@1 score of 74.7% and a DDR score of 97.7%. Although these metrics slightly declined with the adaptive questionnaire. The tool's interpretability is enhanced through global and local explainers, which elucidate the key features influencing each diagnosis. Prototype testing demonstrated VITAS's ability to deliver accurate predictions and symptom explanations within an estimated 2.5 seconds per interaction.

These results indicate that VITAS has potential to alleviate the workload of healthcare professionals and improve patient outcomes, particularly in resource-limited settings. VITAS is positioned as a valuable supplementary diagnosis tool that could transform the diagnostic landscape in the Philippines. Future research should focus on rigorous validation with real-world data and collaborations with medical experts to ensure the tool's reliability and applicability across diverse clinical scenarios.
Includes bibliographic reference.
Keywords: Disease Diagnosis -- Semantic Search -- Adaptive Questionnaire -- Predictive Modeling -- DDXSPlus dataset