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

Houssem Ben Khalfallah; Mariem Jelassi; Jacques Demongeot; Narjès Bellamine Ben Saoud; Houssem Ben Khalfallah; Mariem Jelassi; Jacques Demongeot; Narjès Bellamine Ben Saoud

doi:10.3934/bioeng.2023004

AIMS Bioengineering

2023, Volume 10, Issue 1: 27-52. doi: 10.3934/bioeng.2023004

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Research article Special Issues

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

1.
RIADI Laboratory, ENSI, Manouba University, 2010, La Manouba, Tunisia
2.
AGEIS laboratory, UGA, 38700, La Tronche, France

Received: 22 November 2022 Revised: 19 January 2023 Accepted: 19 January 2023 Published: 02 February 2023

Objective
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.
Methods
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.
Results
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.
Conclusion
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.
- decision support system,
- healthcare,
- clinical decision support system,
- decision-making support systems
Citation: Houssem Ben Khalfallah, Mariem Jelassi, Jacques Demongeot, Narjès Bellamine Ben Saoud. Decision support systems in healthcare: systematic review, meta-analysis and prediction, with example of COVID-19[J]. AIMS Bioengineering, 2023, 10(1): 27-52. doi: 10.3934/bioeng.2023004

Related Papers:

Abstract

Objective

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.

Methods

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.

Results

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.

Conclusion

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.

Abbreviations

DMSS

Decision-making support systems

DSS

decision support systems

Artificial Intelligence

i-DSS

Intelligent decision support systems

EHR

electronic health records

machine learning

ANN

artificial neural network

logistic regression

SVM

support vector machines

naive Bayes

kNN

k-nearest neighbors

LDA

linear discriminant analysis

decision trees

Conflict of interest

The authors declare no conflict of interest.

Author contributions

Contributions of all authors are the same.

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