Skip navigation

A supervised machine learning approach to generate the auto rule for clinical decision support system

A supervised machine learning approach to generate the auto rule for clinical decision support system

Pandey, Sanjib Raj, Ma, Jixin and Lai, Choi-Hong ORCID: 0000-0002-7558-6398 (2020) A supervised machine learning approach to generate the auto rule for clinical decision support system. Trends in Medicine, 20 (3). pp. 1-9. ISSN 1594-2848 (doi:https://doi.org/10.15761/TiM.1000232)

[img]
Preview
PDF (Publisher's PDF - Open Access)
29685 MA_A_Supervised_Machine_Learning_Approach_(OA)_2020.pdf - Published Version
Available under License Creative Commons Attribution.

Download (1MB) | Preview

Abstract

This paper illustrates a prototype for a Clinical Decision Support System (CDSS), using Supervised Machine Learning (SML) to derive rules from pre-constructed cases or to automatically generate rules. We propose an integrated architecture invoking two main components - Rule Pattern Matching Process (RPMP) and Auto Rule Generation Process (ARGP). The RPMP searches for and matches rules from a clinically derived reference set, successful discovery resulting in continued processing through the system. If no rule is found, the AGRP is automatically activated. The AGRP has been designed based on the SML approach. A Decision Tree Algorithm has been used and nested If-else statements applied to transform the decision tree algorithm to generate rules. For experimental purposes, we have developed a prototype and implemented a learning algorithm for generating auto rules for the diagnosis of Acute Rheumatic Fever (ARF). Based on results, the prototype can successfully generate the auto rules for ARF diagnosis. The prototype was designed to classify the ARF stages into “Detected”, “Suspected” and “Not detected”, in addition, it has classifiers capable of classifying the severity levels of detected stage into Severe, Moderate or Mild case. We simulated a set of 104 cases of ARF and observed the rules. The prototype successfully generated the new rule and classified it with the appropriate category (stage). In summary, the applied approach performed extremely well and the developed prototype provided reliable rules for ARF diagnosis. This prototype therefore reduces the task of manually creating ARF diagnosis rules. This approach could be applied in other clinical diagnosis processes.

Item Type: Article
Additional Information: © 2020 Pandey SR. This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Uncontrolled Keywords: Machine Learning, Clinical Decision Support System
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Faculty / Department / Research Group: Faculty of Liberal Arts & Sciences
Faculty of Liberal Arts & Sciences > Centre for Computer & Computational Science
Faculty of Liberal Arts & Sciences > School of Computing & Mathematical Sciences (CAM)
Last Modified: 26 Nov 2020 22:34
Selected for GREAT 2016: None
Selected for GREAT 2017: None
Selected for GREAT 2018: None
Selected for GREAT 2019: None
Selected for REF2021: None
URI: http://gala.gre.ac.uk/id/eprint/29685

Actions (login required)

View Item View Item

Downloads

Downloads per month over past year

View more statistics