Title: A patient-centric decision guidance system for detecting glycemia of diabetes patients

Authors: Chun-Kit Ngan; Lin Li

Addresses: Division of Engineering and Information Science, Great Valley School of Graduate Professional Studies, The Pennsylvania State University, 30 East Swedesford Road, Malvern, PA 19355, USA ' Department of Computer Science and Software Engineering, College of Science and Engineering, Seattle University, 901 12th Avenue Engr 526, Seattle, WA 98122, USA

Abstract: We propose the development of a patient-centric decision guidance system for detecting glycemia of diabetes patients. The proposed approach combines the strengths of both domain-knowledge-based and machine-learning-based approaches to learn the detection parameters. The contributions of this work are four-fold: 1) develop the new extended glycemic expert query parametric estimation (G-EQPE) model and the sequential-parallel-modularised (SPM) architecture to learn the optimal hypo- and hyperglycemic detection parameters simultaneously with a lower computational cost; 2) provide the user-friendly GUI tools and the well-defined JavaScript object notation (JSON) schemas for healthcare system specialists to maintain the system operations; 3) enable the specialists to construct the customised JSONiq queries for data manipulation, data monitoring, parameter learning, and report generation; 4) conduct the experimental study and present the superior detection results in terms of accuracy, sensitivity, and specificity by using the learned detection parameters (i.e., 99 mg/dL and 172 mg/dL).

Keywords: hypoglycemia; hyperglycemia; diabetes patients; diabetics; decision guidance system; optimisation modelling; parameter learning; JavaScript object notation; JSON schemas; JSONiq queries; glycemia detection; domain knowledge; machine learning; healthcare technology; data manipulation; data monitoring; report generation.

DOI: 10.1504/IJADS.2016.081397

International Journal of Applied Decision Sciences, 2016 Vol.9 No.4, pp.366 - 399

Received: 31 Mar 2016
Accepted: 10 Sep 2016
Published online: 06 Jan 2017 *

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