Adding questions about cardiovascular risk factors improve the ability of the ADA questionnaire to identify unknown diabetic patients in Spain

Guisasola, Fernando Álvarez; Conget, Ignacio; Franch, Josep; Mata, M; Mediavilla, Javier; Sarrı́a, Antonio; Soler, Juan; Ramírez, E.; Clerch, Luis

doi:10.1016/s1134-3230(10)65008-9

Cited by 1 publication

(3 citation statements)

References 22 publications

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“…The other studies (n=3) reported either similar [41] or lower performance of ML to classical models. [42][43].…”

Section: Resultsmentioning

confidence: 99%

“…In 27 studies addressing this classification [29,31,67–75,116,33,117,118,129– 133,38,40,42,63–66], populations involved were from 12 countries, mainly the US (41.9%). The studies were published since 2008 with the highest number of studies in 2019 (38.7%).…”

Section: Resultsmentioning

confidence: 99%

“…In 27 studies addressing this classification [29,31,[67][68][69][70][71][72][73][74][75]116,33,117,118,[129][130][131][132][133]38,40,42,[63][64][65][66], populations involved were from 12 countries, mainly the US (41.9%).…”

Section: Health Conditionsmentioning

confidence: 99%

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A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem

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Schauberger

et al. 2022

Preprint

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Aim: With the rapid advances in technology and data science, machine learning (ML) is being adopted by the health care sector; but there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC). To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify the health conditions targeted by ML in PHC. Methods: We searched the Cochrane Library, Web of Science, PubMed, Elsevier, BioRxiv, Association of Computing Machinery (ACM), and IEEE Xplore databases for studies published from January 1990 to January 2022. We included any primary study addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. We performed literature screening, data extraction, and risk of bias assessment. Health conditions were categorized according to international classification of diseases. Extracted date were analyzed quantitatively and qualitatively. Results: We identified 109 studies investigating 42 health conditions. These studies included 273 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 82% of the studies were retrospective. 76.6% of the studies reported diagnostic predictive ML models. 77% of all reported models aimed for models’ development without external validation. Risk of bias assessment revealed that 90.8% of the studies were of high or unclear risk of bias. The most frequently reported health conditions were Alzheimer’s disease and diabetes mellitus. Conclusions: To the best of our knowledge, this is the first review to investigate the extent of the health conditions targeted by the ML prediction models within PHC settings. Our study provides an important summary on the presently available ML models in PHC, which can be used in further research and implementation efforts.

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“…The other studies (n=3) reported either similar [41] or lower performance of ML to classical models. [42][43].…”

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

See 1 more Smart Citation

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

Abdulazeem

Whitelaw

Schauberger

et al. 2022

Preprint

View full text Add to dashboard Cite

show abstract

Adding questions about cardiovascular risk factors improve the ability of the ADA questionnaire to identify unknown diabetic patients in Spain

Cited by 1 publication

References 22 publications

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

A systematic review of clinical health conditions predicted by machine learning diagnostic and prognostic models trained or validated using real-world primary health care data

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