A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.

Salem, H., Soria, D., Lund, J. and Awwad, A. 2021. A systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology. BMC Medical Informatics and Decision Making. 21 (1) 223. https://doi.org/10.1186/s12911-021-01585-9

TitleA systematic review of the applications of Expert Systems (ES) and machine learning (ML) in clinical urology.
TypeJournal article
AuthorsSalem, H., Soria, D., Lund, J. and Awwad, A.
Abstract<h4>Background</h4>Testing a hypothesis for 'factors-outcome effect' is a common quest, but standard statistical regression analysis tools are rendered ineffective by data contaminated with too many noisy variables. Expert Systems (ES) can provide an alternative methodology in analysing data to identify variables with the highest correlation to the outcome. By applying their effective machine learning (ML) abilities, significant research time and costs can be saved. The study aims to systematically review the applications of ES in urological research and their methodological models for effective multi-variate analysis. Their domains, development and validity will be identified.<h4>Methods</h4>The PRISMA methodology was applied to formulate an effective method for data gathering and analysis. This study search included seven most relevant information sources: WEB OF SCIENCE, EMBASE, BIOSIS CITATION INDEX, SCOPUS, PUBMED, Google Scholar and MEDLINE. Eligible articles were included if they applied one of the known ML models for a clear urological research question involving multivariate analysis. Only articles with pertinent research methods in ES models were included. The analysed data included the system model, applications, input/output variables, target user, validation, and outcomes. Both ML models and the variable analysis were comparatively reported for each system.<h4>Results</h4>The search identified n = 1087 articles from all databases and n = 712 were eligible for examination against inclusion criteria. A total of 168 systems were finally included and systematically analysed demonstrating a recent increase in uptake of ES in academic urology in particular artificial neural networks with 31 systems. Most of the systems were applied in urological oncology (prostate cancer = 15, bladder cancer = 13) where diagnostic, prognostic and survival predictor markers were investigated. Due to the heterogeneity of models and their statistical tests, a meta-analysis was not feasible.<h4>Conclusion</h4>ES utility offers an effective ML potential and their applications in research have demonstrated a valid model for multi-variate analysis. The complexity of their development can challenge their uptake in urological clinics whilst the limitation of the statistical tools in this domain has created a gap for further research studies. Integration of computer scientists in academic units has promoted the use of ES in clinical urological research.
KeywordsHumans
Prostatic Neoplasms
Urology
Expert Systems
MEDLINE
Male
Machine Learning
Article number223
JournalBMC Medical Informatics and Decision Making
Journal citation21 (1)
ISSN1472-6947
Year2021
PublisherBMC
Publisher's version
License
CC BY 4.0
File Access Level
Open (open metadata and files)
Digital Object Identifier (DOI)https://doi.org/10.1186/s12911-021-01585-9
PubMed ID34294092
Publication dates
Published online22 Jul 2021
Published in print01 Jul 2021
LicenseCC BY
File12911_2021_Article_1585.pdf

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