%0 Journal Article
%@ 1929-073X
%I JMIR Publications
%V 12
%N 
%P e42540
%T Using Decision Trees as an Expert System for Clinical Decision Support for COVID-19
%A Chrimes,Dillon
%+ School of Health Information Science, Human and Social Development, University of Victoria, HSD Building, A202, Victoria, BC, V8W2Y2, Canada, 1 250 472 4474, dchrimes@uvic.ca
%K assessment tool
%K chatbot
%K clinical decision support
%K COVID-19
%K decision tree
%K digital health tool
%K framework
%K health informatics
%K health intervention
%K prototype
%D 2023
%7 30.1.2023
%9 Viewpoint
%J Interact J Med Res
%G English
%X COVID-19 has impacted billions of people and health care systems globally. However, there is currently no publicly available chatbot for patients and care providers to determine the potential severity of a COVID-19 infection or the possible biological system responses and comorbidities that can contribute to the development of severe cases of COVID-19. This preliminary investigation assesses this lack of a COVID-19 case-by-case chatbot into consideration when building a decision tree with binary classification that was stratified by age and body system, viral infection, comorbidities, and any manifestations. After reviewing the relevant literature, a decision tree was constructed using a suite of tools to build a stratified framework for a chatbot application and interaction with users. A total of 212 nodes were established that were stratified from lung to heart conditions along body systems, medical conditions, comorbidities, and relevant manifestations described in the literature. This resulted in a possible 63,360 scenarios, offering a method toward understanding the data needed to validate the decision tree and highlighting the complicated nature of severe cases of COVID-19. The decision tree confirms that stratification of the viral infection with the body system while incorporating comorbidities and manifestations strengthens the framework. Despite limitations of a viable clinical decision tree for COVID-19 cases, this prototype application provides insight into the type of data required for effective decision support.
%M 36645840
%R 10.2196/42540
%U https://www.i-jmr.org/2023/1/e42540
%U https://doi.org/10.2196/42540
%U http://www.ncbi.nlm.nih.gov/pubmed/36645840