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Fuzzy decision support systems to diagnose musculoskeletal disorders: A systematic literature review
- Source :
- Computer Methods and Programs in Biomedicine. 163:101-109
- Publication Year :
- 2018
- Publisher :
- Elsevier BV, 2018.
-
Abstract
- Background and objective Musculoskeletal disorders (MSDs) are one of the most important causes of disability with a high prevalence. The accurate and timely diagnosis of these disorders is often difficult. Clinical decision support systems (CDSSs) can help physicians to diagnose diseases quickly and accurately. Given the ambiguous nature of MSDs, fuzzy logic can be helpful in designing the CDSSs knowledge bases. The present study aimed to review the studies on fuzzy CDSSs to diagnose MSDs. Methods A comprehensive search was conducted in Medline, Scopus, Cochrane Library, and ISI Web of Science databases to identify relevant studies published until March 15, 2016. Studies were included in which CDSSs were developed using fuzzy logic to diagnose MSDs, and tested their accuracy using real data from patients. Results Of the 3188 papers examined, 23 papers included according to the inclusion criteria. The results showed that among all the designed CDSSs only one (CADIAG-2) was implemented in the clinical environment. In about half of the included studies (52%), CDSSs were designed to diagnose inflammatory/infectious disorder of the bone and joint. In most of the included studies (70%), the knowledge was extracted using a combination of three methods (acquiring from experts, analyzing the data, and reviewing the literature). The median accuracy of fuzzy rule-based CDSSs was 91% and it was 90% for other fuzzy models. The most frequently used membership functions were triangular and trapezoidal functions, and the most used method for inference was the Mamdani. Conclusions In general, fuzzy CDSSs have a high accuracy to diagnose MSDs. Despite the high accuracy, these systems have been used to a limited extent in the clinical environments. To design of knowledge base for CDSSs to diagnose MSDs, rule-based methods are used more than other fuzzy methods.
- Subjects :
- Decision support system
020205 medical informatics
Computer science
MEDLINE
Inference
Health Informatics
02 engineering and technology
Cochrane Library
Machine learning
computer.software_genre
Risk Assessment
Clinical decision support system
Fuzzy logic
Fuzzy Logic
Prevalence
0202 electrical engineering, electronic engineering, information engineering
Humans
Musculoskeletal Diseases
Inflammation
Fuzzy rule
business.industry
Publications
Reproducibility of Results
Decision Support Systems, Clinical
Computer Science Applications
Systematic review
Knowledge base
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Software
Subjects
Details
- ISSN :
- 01692607
- Volume :
- 163
- Database :
- OpenAIRE
- Journal :
- Computer Methods and Programs in Biomedicine
- Accession number :
- edsair.doi.dedup.....976f08bfb8410ac293516aa41543b6b5
- Full Text :
- https://doi.org/10.1016/j.cmpb.2018.06.002