Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools

Abstract

Clinical Decision Support Systems (CDSS) offer the potential to improve quality of clinical care and patients’ outcomes while reducing medical errors and economic costs. The development of these systems results difficult since (i) generating the knowledge base that CDSS use to evaluate clinical data requires technical and clinical knowledge, and (ii) usually the reasoning process of CDSS is difficult to understand for clinicians leading to a low adherence to the recommendations provided by these systems. Hereafter, to address these issues, we propose a web-based platform, named Knowledge Generation Tool (KGT), which (i) enables clinicians to take an active role in the creation of the CDSSs in a simple way, and (ii) clinicians’ involvement can turn in an improvement of the model predictor capabilities, while their comprehension of the reasoning process of the CDSS is increased. The KGT consist on three main modules: DT building, which implements machine learning methods to extract automatically decision trees (DTs) from clinical data frames; an authoring tool (AT), which enables the clinicians to modify the DT with their expert knowledge, and the DT testing, which allows to test any DT, being able to test objectively any modification made by clinician’s expert knowledge.

BIB_text

@Article {
title = {Adaptative Clinical Decision Support System using Machine Learning and Authoring Tools},
pages = {95-105},
keywds = {
Authoring Tool, Machine Learning, CDSS, Decision Tree
}
abstract = {

Clinical Decision Support Systems (CDSS) offer the potential to improve quality of clinical care and patients’ outcomes while reducing medical errors and economic costs. The development of these systems results difficult since (i) generating the knowledge base that CDSS use to evaluate clinical data requires technical and clinical knowledge, and (ii) usually the reasoning process of CDSS is difficult to understand for clinicians leading to a low adherence to the recommendations provided by these systems. Hereafter, to address these issues, we propose a web-based platform, named Knowledge Generation Tool (KGT), which (i) enables clinicians to take an active role in the creation of the CDSSs in a simple way, and (ii) clinicians’ involvement can turn in an improvement of the model predictor capabilities, while their comprehension of the reasoning process of the CDSS is increased. The KGT consist on three main modules: DT building, which implements machine learning methods to extract automatically decision trees (DTs) from clinical data frames; an authoring tool (AT), which enables the clinicians to modify the DT with their expert knowledge, and the DT testing, which allows to test any DT, being able to test objectively any modification made by clinician’s expert knowledge.


}
isbn = {978-989-758-398-8},
date = {2020-03-18},
}
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