Imperial College London
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Dr. Alexander R.M. Lyons, Ph.D

Faculty of Medicine, School of Public Health

Research Associate/Project Manager
 
 
 
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Contact

 

+44 (0)20 7594 2771a.lyons

 
 
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Location

 

Reynolds BuildingCharing Cross Campus

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Summary

 

Publications

Citation

BibTex format

@article{Stevens:2023,
author = {Stevens, C and Lyons, A and Dharmayat, K and Mahani, A and Ray, K and Vallejo-Vaz, AJ and Taghavi, Azar Sharabiani M},
journal = {Digital Health},
pages = {1--17},
title = {Ensemble machine learning methods in screening electronic health records: a scoping review},
url = {https://journals.sagepub.com/doi/full/10.1177/20552076231173225},
volume = {9},
year = {2023}
}
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RIS format (EndNote, RefMan)

TY  - JOUR
AB  - Background:Electronic Health Records (EHRs) provide the opportunity to identify undiagnosed individuals likely to have a given disease using Machine Learning (ML) techniques, and who could then benefit from more medical screening and case finding, reducing the number needed to screen with convenience and healthcare cost savings. Ensemble Machine Learning Models (EMLs) combining multiple prediction estimates into one, are often said to provide better predictive performances than non-ensemble models. Yet, to our knowledge, no literature review summarises the use and performances of different types of EMLs in the context of medical pre-screening. Method:We aimed to conduct a scoping review of the literature reporting the derivation of EMLs for screening of EHRs. We searched EMBASE and MEDLINE databases across all years applying a formal search strategy using terms related to medical screening, EHR and ML. Data were collected, analysed, and reported in accordance with the PRISMA scoping review guideline. Results:A total of 3,355 articles were retrieved, of which 145 articles met our inclusion criteria and were included in this study. EMLs were increasingly employed across several medical specialities and often outperformed non-ensemble approaches. EMLs with complex combination strategies and heterogeneous classifiers often outperformed other types of EMLs but were also less used. EML methodologies, processing steps and data sources were often not clearly described. Conclusions:Our work highlights the importance of deriving and comparing the performances of different types of EMLs when screening EHRs and underscores the need for more comprehensive reporting of ML methodologies employed in clinical research.
AU  - Stevens,C
AU  - Lyons,A
AU  - Dharmayat,K
AU  - Mahani,A
AU  - Ray,K
AU  - Vallejo-Vaz,AJ
AU  - Taghavi,Azar Sharabiani M
EP  - 17
PY  - 2023///
SN  - 2055-2076
SP  - 1
TI  - Ensemble machine learning methods in screening electronic health records: a scoping review
T2  - Digital Health
UR  - https://journals.sagepub.com/doi/full/10.1177/20552076231173225
UR  - http://hdl.handle.net/10044/1/103971
VL  - 9
ER  -
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