Finding Undiagnosed Patients With Familial Hypercholesterolemia in Primary Care Usingelectronic Health Records

Williamson, Latoya; Wójcik, Cezary; Taunton, Marshall; McElheran, Kylie; Howard, William H.; Staszak, Dave; Gidding, Samuel S.; Wilemon, Katherine; Myers, Kelly; Shapiro, Michael D.

doi:10.1016/s0735-1097(20)34129-2

Cited by 3 publications

(3 citation statements)

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“…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%

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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

Whitelaw

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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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%

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

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“…Totally 42 articles used NLP tools including Apache cTAKES, MetaMap, Moonstone, MTERMS, BioMedICUS, MediClass, and LEO [ 14 - 16 , 20 , 22 , 26 , 31 - 35 , 38 , 40 - 42 , 43 , 45 , 46 , 48 , 51 , 59 , 61 , 62 , 68 , 69 , 71 , 73 , 74 , 76 - 81 , 83 , 84 , 86 , 87 , 90 , 94 , 95 ]. Apache cTAKES was developed on the UIMA platform and Apache openNLP toolkit and is one of the most popular NLP tools for clinical IE from EHR data [ 96 ].…”

Section: Resultsmentioning

confidence: 99%

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

et al. 2021

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Background. There is growing evidence that social and behavioral determinants of health (SBDH) play a substantial effect in a wide range of health outcomes. Electronic health records (EHRs) have been widely employed to conduct observational studies in the age of artificial intelligence (AI). However, there has been limited review into how to make the most of SBDH information from EHRs using AI approaches. Methods. A systematic search was conducted in six databases to find relevant peer-reviewed publications that had recently been published. Relevance was determined by screening and evaluating the articles. Based on selected relevant studies, a methodological analysis of AI algorithms leveraging SBDH information in EHR data was provided. Results. Our synthesis was driven by an analysis of SBDH categories, the relationship between SBDH and healthcare-related statuses, natural language processing (NLP) approaches for extracting SBDH from clinical notes, and predictive models using SBDH for health outcomes. Discussion. The associations between SBDH and health outcomes are complicated and diverse; several pathways may be involved. Using NLP technology to support the extraction of SBDH and other clinical ideas simplifies the identification and extraction of essential concepts from clinical data, efficiently unlocks unstructured data, and aids in the resolution of unstructured data-related issues. Conclusion. Despite known associations between SBDH and diseases, SBDH factors are rarely investigated as interventions to improve patient outcomes. Gaining knowledge about SBDH and how SBDH data can be collected from EHRs using NLP approaches and predictive models improves the chances of influencing health policy change for patient wellness, ultimately promoting health and health equity.

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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,

Whitelaw,

Schauberger

et al. 2023

PLoS ONE

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With the advances in technology and data science, machine learning (ML) is being rapidly adopted by the health care sector. However, there is a lack of literature addressing the health conditions targeted by the ML prediction models within primary health care (PHC) to date. To fill this gap in knowledge, we conducted a systematic review following the PRISMA guidelines to identify health conditions targeted by ML in PHC. 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 primary studies addressing ML diagnostic or prognostic predictive models that were supplied completely or partially by real-world PHC data. Studies selection, data extraction, and risk of bias assessment using the prediction model study risk of bias assessment tool were performed by two investigators. Health conditions were categorized according to international classification of diseases (ICD-10). Extracted data were analyzed quantitatively. We identified 106 studies investigating 42 health conditions. These studies included 207 ML prediction models supplied by the PHC data of 24.2 million participants from 19 countries. We found that 92.4% of the studies were retrospective and 77.3% of the studies reported diagnostic predictive ML models. A majority (76.4%) of all the studies were for models’ development without conducting 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 diabetes mellitus (19.8%) and Alzheimer’s disease (11.3%). Our study provides a summary on the presently available ML prediction models within PHC. We draw the attention of digital health policy makers, ML models developer, and health care professionals for more future interdisciplinary research collaboration in this regard.

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Finding Undiagnosed Patients With Familial Hypercholesterolemia in Primary Care Usingelectronic Health Records

Cited by 3 publications

References 0 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

Social and Behavioral Determinants of Health in the Era of Artificial Intelligence with Electronic Health Records: A Scoping Review

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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