Clinical Decision Support System Based on Hybrid Knowledge Modeling: A Case Study of Chronic Kidney Disease-Mineral and Bone Disorder Treatment

Syed Imran Ali, Su Woong Jung, Hafiz Syed Muhammad Bilal, Sang-Ho Lee, Jamil Hussain, Muhammad Afzal, Maqbool Hussain, Taqdir Ali, Taechoong Chung and Sungyoung Lee
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Syed Imran Ali: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea
Su Woong Jung: Department of Internal Medicine, Division of Nephrology, Kyung Hee University Hospital at Gangdong, Seoul 05278, Korea
Hafiz Syed Muhammad Bilal: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea
Sang-Ho Lee: Department of Internal Medicine, Division of Nephrology, Kyung Hee University Hospital at Gangdong, Seoul 05278, Korea
Jamil Hussain: Department of Data Science, Sejong University, Seoul 30019, Korea
Muhammad Afzal: Department of Software, Sejong University, Seoul 30019, Korea
Maqbool Hussain: Department of Software, Sejong University, Seoul 30019, Korea
Taqdir Ali: BC Children’s Hospital, University of British Columbia, Vancouver, BC V6H 3N1, Canada
Taechoong Chung: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea
Sungyoung Lee: Department of Computer Science and Engineering, Kyung Hee University, Yongin-si 17104, Korea

IJERPH, 2021, vol. 19, issue 1, 1-28

Abstract: Clinical decision support systems (CDSSs) represent the latest technological transformation in healthcare for assisting clinicians in complex decision-making. Several CDSSs are proposed to deal with a range of clinical tasks such as disease diagnosis, prescription management, and medication ordering. Although a small number of CDSSs have focused on treatment selection, areas such as medication selection and dosing selection remained under-researched. In this regard, this study represents one of the first studies in which a CDSS is proposed for clinicians who manage patients with end-stage renal disease undergoing maintenance hemodialysis, almost all of whom have some manifestation of chronic kidney disease–mineral and bone disorder (CKD–MBD). The primary objective of the system is to aid clinicians in dosage prescription by levering medical domain knowledge as well existing practices. The proposed CDSS is evaluated with a real-world hemodialysis patient dataset acquired from Kyung Hee University Hospital, South Korea. Our evaluation demonstrates overall high compliance based on the concordance metric between the proposed CKD–MBD CDSS recommendations and the routine clinical practice. The concordance rate of overall medication dosing selection is 78.27%. Furthermore, the usability aspects of the system are also evaluated through the User Experience Questionnaire method to highlight the appealing aspects of the system for clinicians. The overall user experience dimension scores for pragmatic, hedonic, and attractiveness are 1.53, 1.48, and 1.41, respectively. A service reliability for the Cronbach’s alpha coefficient greater than 0.7 is achieved using the proposed system, whereas a dependability coefficient of the value 0.84 reveals a significant effect.

Keywords: clinical decision support system; treatment recommendation; case-based reasoning; medication dosing estimation; expert knowledge modeling (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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