Evaluation of Stream Mining Classifiers for Real-Time Clinical Decision Support System: A Case Study of Blood Glucose Prediction in Diabetes Therapy

Abstract

Earlier on, a conceptual design on the real-time clinical decision support system (rt-CDSS) with data stream mining was proposed and published. The new system is introduced that can analyze medical data streams and can make real-time prediction. This system is based on a stream mining algorithm called VFDT. The VFDT is extended with the capability of using pointers to allow the decision tree to remember the mapping relationship between leaf nodes and the history records. In this paper, which is a sequel to the rt-CDSS design, several popular machine learning algorithms are investigated for their suitability to be a candidate in the implementation of classifier at the rt-CDSS. A classifier essentially needs to accurately map the events inputted to the system into one of the several predefined classes of assessments, such that the rt-CDSS can follow up with the prescribed remedies being recommended to the clinicians. For a real-time system like rt-CDSS, the major technological challenges lie in the capability of the classifier to process, analyze and classify the dynamic input data, quickly and upmost reliably. An experimental comparison is conducted. This paper contributes to the insight of choosing and embedding a stream mining classifier into rt-CDSS with a case study of diabetes therapy.

Machine Learning and Data Mining Methods in Diabetes Research

“…The second category deals with disease prediction and diagnosis [40], [41], [42], [43], [44], [45], [46], [47], [48], [49], [50], [51], [52], [53], [54], [55], [56], [57], [58], [59], [60], [61], [62], [63], [64], [65], [66], [67], [68], [69], [70], [71], [72], [73], [74], [75], [76]. Numerous algorithms and different approaches have been applied, such as traditional machine learning algorithms, ensemble learning approaches and association rule learning in order to achieve the best classification accuracy.…”

“…In [76], authors proposed a fuzzy ontology-based Case-based reasoning (CBR) framework, mimicking expert thinking, further tested on diabetes diagnosis problems. In [58], authors performed an evaluation of Stream Mining Classifiers for Real-time Clinical Decision Support Systems.…”

Development of a clinical decision support system for diabetes care: A pilot study

“…CDSS designs can differ significantly in content and scope [ 18 ]. They have been used to automate test and treatment recommendations [ 19 , 20 ], assist in risk stratification for diabetic foot screening [ 21 ], promote health communication with patients [ 22 ], predict blood glucose [ 23 ], interpret self-monitoring of blood glucose data [ 24 , 25 ], monitor guideline adherence [ 26 ], correct/ prevent medication error [ 27 ], and detect potential adverse drug interactions [ 28 ].…”

Technical Requirement of Clinical Decision Support system for Diabetic Patients

“…Decision support tools can help clinicians with the inspection of monitoring data, providing a preliminary analysis to ease their interpretation and reduce the evaluation time per patient [2]. Clinical decision support system (CDSS) is a computer tool which broadly covers autonomous or semiautonomous tasks ranging among symptoms diagnosis, analysis, classification, and computer-aided reasoning on choosing some appropriate medical care or treatment [8]. a CDSS can be defined as "a system that is designed to be a direct aid to clinical decision-making in which the characteristics of an individual patient are matched to a computerized clinical knowledge base, and patient-specific assessments or recommendations are then presented to the clinician(s) and/or the patient for a decision" [8].…”

“…Clinical decision support system (CDSS) is a computer tool which broadly covers autonomous or semiautonomous tasks ranging among symptoms diagnosis, analysis, classification, and computer-aided reasoning on choosing some appropriate medical care or treatment [8]. a CDSS can be defined as "a system that is designed to be a direct aid to clinical decision-making in which the characteristics of an individual patient are matched to a computerized clinical knowledge base, and patient-specific assessments or recommendations are then presented to the clinician(s) and/or the patient for a decision" [8]. CDSSs, at the simplest level, are tools to help clinicians and patients make better informed decisions during use of the EHR.…”