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Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?

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Abstract

Dementia and mild cognitive impairment can be underrecognized in primary care practice and research. Free-text fields in electronic medical records (EMRs) are a rich source of information which might support increased detection and enable a better understanding of populations at risk of dementia. We used natural language processing (NLP) to identify dementia-related features in EMRs and compared the performance of supervised machine learning models to classify patients with dementia. We assembled a cohort of primary care patients aged 66 + years in Ontario, Canada, from EMR notes collected until December 2016: 526 with dementia and 44,148 without dementia. We identified dementia-related features by applying published lists, clinician input, and NLP with word embeddings to free-text progress and consult notes and organized features into thematic groups. Using machine learning models, we compared the performance of features to detect dementia, overall and during time periods relative to dementia case ascertainment in health administrative databases. Over 900 dementia-related features were identified and grouped into eight themes (including symptoms, social, function, cognition). Using notes from all time periods, LASSO had the best performance (F1 score: 77.2%, sensitivity: 71.5%, specificity: 99.8%). Model performance was poor when notes written before case ascertainment were included (F1 score: 14.4%, sensitivity: 8.3%, specificity 99.9%) but improved as later notes were added. While similar models may eventually improve recognition of cognitive issues and dementia in primary care EMRs, our findings suggest that further research is needed to identify which additional EMR components might be useful to promote early detection of dementia.

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Data Availability

The dataset from this study is held securely in coded form at ICES. While legal data sharing agreements between ICES and data providers (e.g., healthcare organizations and government) prohibit ICES from making the dataset publicly available, access may be granted to those who meet pre-specified criteria for confidential access, available at www.ices.on.ca/DAS (email: das@ices.on.ca).

Code Availability

The full dataset creation plan and underlying analytic code are available from the authors upon request, understanding that the computer programs may rely upon coding templates or macros that are unique to ICES and are therefore either inaccessible or may require modification.

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Acknowledgements

This study was supported by ICES, which is funded by an annual grant from the Ontario Ministry of Health (MOH) and the Ministry of Long-Term Care (MLTC). This document used data adapted from the Statistics Canada Postal CodeOM Conversion File, which is based on data licensed from Canada Post Corporation, and/or data adapted from the Ontario Ministy of Health Postal Code Conversion File, which contains data copied under license from ©Canada Post Corporation and Statistics Canada. Parts of this material are basesd on data and information compiled and provided by CIHI and the Ontario Ministry of Health. We thank IQVIA Solutions Canada Inc. for the use of their Drug Information File.

Funding

This study was supported by the Ontario Neurodegenerative Disease Research Initiative (ONDRI) through the Ontario Brain Institute, an independent non-profit corporation, funded partially by the Ontario government. MA is funded by the Canadian Institutes of Health Research Vanier Scholarship Program. DAH is funded by an Alzheimer Society of Canada Research Program Doctoral Award.

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Authors and Affiliations

  1. ICES, G1-06, 2075 Bayview Avenue, Toronto, M4N 3M5, Canada

    Laura C. Maclagan, Therese A. Stukel, Branson Chen, Elisa Candido, Richard H. Swartz, R. Liisa Jaakkimainen & Susan E. Bronskill

  2. Department of Computer Science, University of Toronto, Toronto, Canada

    Mohamed Abdalla

  3. Division of Epidemiology, Dalla Lana School of Public Health, University of Toronto, Toronto, Canada

    Daniel A. Harris & Susan E. Bronskill

  4. Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada

    Therese A. Stukel, R. Liisa Jaakkimainen & Susan E. Bronskill

  5. Sunnybrook Research Institute, Sunnybrook Health Sciences Centre, Toronto, Canada

    Therese A. Stukel, Richard H. Swartz, R. Liisa Jaakkimainen & Susan E. Bronskill

  6. Department of Medicine (Neurology), Sunnybrook Health Sciences Centre, University of Toronto, Toronto, Canada

    Richard H. Swartz

  7. KITE Research Institute, Toronto Rehabilitation Institute, University Health Network, Toronto, Canada

    Andrea Iaboni

  8. Department of Psychiatry, University of Toronto, Toronto, Canada

    Andrea Iaboni

  9. Department of Family and Community Medicine, University of Toronto, Toronto, Canada

    R. Liisa Jaakkimainen

  10. Women’s College Research Institute, Women’s College Hospital, Toronto, Canada

    Susan E. Bronskill

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  1. Laura C. Maclagan
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Correspondence to Susan E. Bronskill.

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Maclagan, L.C., Abdalla, M., Harris, D.A. et al. Can Patients with Dementia Be Identified in Primary Care Electronic Medical Records Using Natural Language Processing?. J Healthc Inform Res 7, 42–58 (2023). https://doi.org/10.1007/s41666-023-00125-6

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