BackgroundElectronic health records provide the opportunity to identify undiagnosed
individuals likely to have a given disease using machine learning
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 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 ensemble
machine learning models in the context of medical pre-screening.MethodWe aimed to conduct a scoping review of the literature reporting the
derivation of ensemble machine learning models for screening of electronic
health records. We searched EMBASE and MEDLINE databases across all years
applying a formal search strategy using terms related to medical screening,
electronic health records and machine learning. Data were collected,
analysed, and reported in accordance with the PRISMA scoping review
guideline.ResultsA total of 3355 articles were retrieved, of which 145 articles met our
inclusion criteria and were included in this study. Ensemble machine
learning models were increasingly employed across several medical
specialties and often outperformed non-ensemble approaches. Ensemble machine
learning models with complex combination strategies and heterogeneous
classifiers often outperformed other types of ensemble machine learning
models but were also less used. Ensemble machine learning models
methodologies, processing steps and data sources were often not clearly
described.ConclusionsOur work highlights the importance of deriving and comparing the performances
of different types of ensemble machine learning models when screening
electronic health records and underscores the need for more comprehensive
reporting of machine learning methodologies employed in clinical
research.