Dual‐stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation

Algorithmic tools for detection of individuals with dementia are available; however, previous work has used heterogeneous case definitions which are not clinically meaningful, and has relied on proxies such as diagnostic codes or medications for case ascertainment. We used a novel, dual-stream algorithmic development approach, simultaneously and separately modeling a clinically meaningful outcome (diagnosis of dementia according to specialized clinical impression) using structured and unstructured electronic health record datasets. Our clinically grounded case definition supported the inclusion of key structured variables (such as dementia International Classification of Disease codes and medications) as modeling predictors rather than outcomes. Our algorithms, published in detail to support validation and replication, represent a major step forward in the use of routinely collected data for detection of diagnosed dementia.

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Publication Details

Journal
Alzheimer s & Dementia
Published
2025-05-01
DOI
https://doi.org/10.1002/alz.70132
Citations
2
Primary Topic
Biomedical Text Mining and Ontologies
Type
article
Field-Weighted Citation Impact
1.61

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article

Dual‐stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation

Trevor T.‐J. Chong, Anne M. Murray, Bridget O’Bree, David Ung et al.
2 citations
Alzheimer s & Dementia
Biomedical Text Mining and Ontologies
1.61
article

Dual‐stream algorithms for dementia detection: Harnessing structured and unstructured electronic health record data, a novel approach to prevalence estimation

Trevor T.‐J. Chong, Anne M. Murray, Bridget O’Bree, David Ung, Chris Moran, Jenni Ilomäki, Nadine E. Andrew, Walter A. Rocca, Amanda Gay Thrift, Taya Collyer, Tanya Ravipati, Alicia Lu, J. Simon Bell, Kristy Siostrom, Richard Beare, Jennifer L. St. Sauver, Velandai Srikanth, Alison Carver, Ming Liu, Helene E. Roberts
article en
2 citations

Abstract

Algorithmic tools for detection of individuals with dementia are available; however, previous work has used heterogeneous case definitions which are not clinically meaningful, and has relied on proxies such as diagnostic codes or medications for case ascertainment. We used a novel, dual-stream algorithmic development approach, simultaneously and separately modeling a clinically meaningful outcome (diagnosis of dementia according to specialized clinical impression) using structured and unstructured electronic health record datasets. Our clinically grounded case definition supported the inclusion of key structured variables (such as dementia International Classification of Disease codes and medications) as modeling predictors rather than outcomes. Our algorithms, published in detail to support validation and replication, represent a major step forward in the use of routinely collected data for detection of diagnosed dementia.

Alzheimer s & DementiaVol. 21(5)
University of Minnesota (US), Mayo Clinic (US), Monash Medical Centre (AU), Peninsula Health (AU), Monash Health (AU), Berman Center for Outcomes and Clinical Research (US), Mayo Clinic in Florida (US), Murdoch Children's Research Institute (AU), Hennepin Healthcare Research Institute (US), Monash University (AU)
National Institutes of Health, National Institute on Aging
Openalex Percentile: Top 17%
Biomedical Text Mining and Ontologies
1.61
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