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Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System

Integrating Knowledge-Driven and Data-Driven Methodologies for an Efficient Clinical Decision Support System
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Author(s): Okure Udo Obot (Department of Computer Science, University of Uyo, Nigeria), Kingsley Friday Attai (Ritman University, Ikot Ekpene, Nigeria)and Gregory O. Onwodi (National Open University of Nigeria, Nigeria)
Copyright: 2023
Pages: 28
Source title: Diverse Perspectives and State-of-the-Art Approaches to the Utilization of Data-Driven Clinical Decision Support Systems
Source Author(s)/Editor(s): Thomas M. Connolly (DS Partnership, UK), Petros Papadopoulos (University of Strathclyde, UK)and Mario Soflano (Glasgow Caledonian University, UK)
DOI: 10.4018/978-1-6684-5092-5.ch001

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Abstract

Clinical decision support systems (CDSSs) symbolize a significant transformation in healthcare delivery. CDSS enhances healthcare delivery by enabling personnel in medical institutions to handle complex decision-making processes with great speed and high accuracy. Decision support systems are developed using a knowledge-driven or data-driven approach, although both approaches seem to complement each other. For instance, while data-driven is an objective approach, the knowledge-driven approach is subjective. The objective of the chapter is to elaborate on the integration of data-driven and knowledge-driven methodologies for clinical decision support systems. An overview of data-driven and knowledge-driven approaches is presented with a review of both current and dated literature on the subject with numerous viewpoints to support the discussion. Based on the findings, a promising methodology is proposed that integrates data-driven and knowledge-driven approaches and is believed to overcome the challenges of the individual approaches.

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