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

• Published in: International journal of environmental research and public health Vol. 19; no. 1; p. 226
• Main Authors: Ali, Syed Imran, Jung, Su Woong, Bilal, Hafiz Syed Muhammad, Lee, Sang-Ho, Hussain, Jamil, Afzal, Muhammad, Hussain, Maqbool, Ali, Taqdir, Chung, Taechoong, Lee, Sungyoung
• Format: Journal Article
• Language: English
• Published: Switzerland MDPI AG 26-12-2021; MDPI
• Subjects: Artificial intelligence; Bone diseases; Calcification; Case studies; Chronic Kidney Disease-Mineral and Bone Disorder; Decision making; Decision support systems; Decision Support Systems, Clinical; Deep learning; Delivery of Health Care; Dosage; Drug dosages; End-stage renal disease; Explicit knowledge; Health services; Hemodialysis; Humans; Kidney diseases; Kidneys; Laboratories; Patients; Prescriptions; Reproducibility of Results; Subject specialists; Usability; User experience; clinical decision support system; case-based reasoning; medication dosing estimation; treatment recommendation; expert knowledge modeling
• ISSN: 1660-4601; 1661-7827; 1660-4601
• DOI: 10.3390/ijerph19010226

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.