Decision support systems in healthcare: systematic review, meta-analysis and prediction, with example of COVID-19

Author:

Khalfallah Houssem Ben1,Jelassi Mariem1,Demongeot Jacques2,Saoud Narjès Bellamine Ben1

Affiliation:

1. RIADI Laboratory, ENSI, Manouba University, 2010, La Manouba, Tunisia

2. AGEIS laboratory, UGA, 38700, La Tronche, France

Abstract

<abstract><sec> <title>Objective</title> <p>The objective of this study was to provide an overview of Decision Support Systems (DSS) applied in healthcare used for diagnosis, monitoring, prediction and recommendation in medicine.</p> </sec><sec> <title>Methods</title> <p>We conducted a systematic review using PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analysis) guidelines of articles published until September 2022 from PubMed, Cochrane, Scopus and web of science databases. We used KH coder to analyze included research. Then we categorized decision support systems based on their types and medical applications.</p> </sec><sec> <title>Results</title> <p>The search strategy provided a total of 1605 articles in the studied period. Of these, 231 articles were included in this qualitative review. This research was classified into 4 categories based on the DSS type used in healthcare: Alert Systems, Monitoring Systems, Recommendation Systems and Prediction Systems. Under each category, domain applications were specified according to the disease the system was applied to.</p> </sec><sec> <title>Conclusion</title> <p>In this systematic review, we collected CDSS studies that use ML techniques to provide insights into different CDSS types. We highlighted the importance of ML to support physicians in clinical decision-making and improving healthcare according to their purposes.</p> </sec></abstract>

Publisher

American Institute of Mathematical Sciences (AIMS)

Reference121 articles.

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