Evidence map›Paper›PMID 40222334›Full record

ArticleInternational journal of medical informatics2025

Automated phenotyping of mild cognitive impairment and Alzheimer's disease and related dementias using electronic health records.

Ruoqi Wei, Stephanie S Buss, Rebecca Milde, Marta Fernandes, Daniel Sumsion, Elijah Davis, Wan-Yee Kong, Yiwen Xiong, Jet Veltink, Samvrit Rao and 21 more

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Article in International journal of medical informatics, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed
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1 · What the graph read from it

What it found

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2 · The registry

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3 · Its place in the literature

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2 citing papers in PubMed.

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4 · The record

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5 · Who and what money

Authors and funding

31 authors.

Ruoqi WeiBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA; Department of Health Outcomes and Biomedical Informatics, College of Medicine, University of Florida, FL, USA.
Stephanie S BussBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Rebecca MildeBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Marta FernandesBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Daniel SumsionBeth Israel Deaconess Medical Center, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA.
Elijah DavisBeth Israel Deaconess Medical Center, Boston, MA, USA; Brigham Young University, Provo, UT 84602, USA.
Wan-Yee KongBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Yiwen XiongBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Jet VeltinkBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Samvrit RaoBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Tara M WestoverMassachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Lydia PetersenBeth Israel Deaconess Medical Center, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA.
Niels TurleyBeth Israel Deaconess Medical Center, Boston, MA, USA.
Arjun SinghMassachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Sudeshna DasBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Valdery Moura JuniorBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Manohar GhantaBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Aditya GuptaBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Jennifer KimYale School of Medicine, New Haven, CT, USA.
Alice D LamMassachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Katie L StoneCalifornia Pacific Medical Center Research Institute, San Francisco, CA, USA; Department of Epidemiology and Biostatistics, University of California, San Francisco, USA.
Emmanuel MignotStanford University, Palo Alto, CA, USA.
Dennis HwangKaiser Permanente, San Bernardino County Sleep Disorders Center. San Bernardino, CA, USA.
Lynn Marie TrottiEmory University School of Medicine, Atlanta, GA, USA.
Gari D CliffordEmory University School of Medicine, Atlanta, GA, USA; Georgia Institute of Technology, Atlanta, GA, USA.
Umakanth KatwaHarvard Medical School, Boston, MA, USA; Boston Children's Hospital, Boston, MA, USA.
Robert J ThomasBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Shibani MukerjiBeth Israel Deaconess Medical Center, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
Sahar F ZafarMassachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA.
M Brandon WestoverBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: bwestove@bidmc.harvard.edu.
Haoqi SunBeth Israel Deaconess Medical Center, Boston, MA, USA; Massachusetts General Hospital, Boston, MA, USA; Harvard Medical School, Boston, MA, USA. Electronic address: hsun3@bidmc.harvard.edu.

Funding

Late-onset Unexplained Epilepsy as a Risk Factor for DementiaR01NS130119 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI Alice D Lam · 2023 to 2026
$8.7M
Data-Driven Sleep Biomarkers of Brain Health, Heart Health, and MortalityR01HL161253 · NHLBI · BETH ISRAEL DEACONESS MEDICAL CENTER · PI CLIFFORD, GARI DAVID, MIGNOT, EMMANUEL J · 2022 to 2025
$8.3M
Comparative Safety of Seizure Prophylaxis within the Medicare ProgramR01AG073410 · NIA · MASSACHUSETTS GENERAL HOSPITAL · PI Lidia Maria Veras Rocha de Moura · 2021 to 2026
$4.1M
Low Neurophysiologic Resistance to Anesthetics as a Marker of Preclinical/Prodromal Alzheimer's Disease and Neurovascular Pathology, Delirium risk and InattentionR01AG073598 · NIA · STANFORD UNIVERSITY · PI Miles Berger · 2022 to 2026
$3.8M
Integrative Motor Activity Biomarker for the Risk of Alzheimer's RiskRF1AG064312 · NIA · BRIGHAM AND WOMEN'S HOSPITAL · PI HU, KUN · 2019 to 2019
$3.6M
Multimodal Network Connectivity Architecture (MOCA) of the Brain and its Role in the Recovery of Consciousness in Comatose Cardiac Arrest PatientsR01NS102574 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI GREER, DAVID MATTHEW, WU, ONA · 2018 to 2022
$3.4M
Investigation of Sleep in the Intensive Care Unit (ICU-SLEEP)R01NS102190 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$3.2M
Prospective Validation of Neurophysiologic Outcome Prediction in Acute Brain InjuryR01NS126282 · NINDS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI Aaron F Struck, Michael Brandon Westover · 2023 to 2026
$2.9M
Big Data and Deep Learning for the Interictal-Ictal-Injury ContiuumR01NS107291 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI WESTOVER, MICHAEL BRANDON · 2018 to 2022
$2.8M
Establishing a Brain Health Index from the Sleep ElectroencephalogramRF1NS120947 · NINDS · MASSACHUSETTS GENERAL HOSPITAL · PI CASH, SYDNEY S, THOMAS, ROBERT JOSEPH · 2021 to 2021
$2.4M
Establishing a Brain Health IndexR01NS120947 · NINDS · BETH ISRAEL DEACONESS MEDICAL CENTER · PI CASH, SYDNEY S, THOMAS, ROBERT JOSEPH · 2024 to 2025
$1.6M
NHLBI NIH HHS R01 HL161253NIA NIH HHS R01 AG073410NIA NIH HHS R01 AG073598NIA NIH HHS RF1 AG064312NINDS NIH HHS R01 NS102190NINDS NIH HHS R01 NS102574NINDS NIH HHS R01 NS107291NINDS NIH HHS R01 NS120947NINDS NIH HHS R01 NS126282NINDS NIH HHS R01 NS130119NINDS NIH HHS RF1 NS120947
6 · The paper itself

Abstract

objectivesUnstructured and structured data in electronic health records (EHR) are a rich source of information for research and quality improvement studies. However, extracting accurate information from EHR is labor-intensive. Timely and accurate identification of patients with Alzheimer's Disease, related dementias (ADRD), or mild cognitive impairment (MCI) is critical for improving patient outcomes through early intervention, optimizing care plans, and reducing healthcare system burdens. Here we introduce an automated EHR phenotyping model to streamline this process and enable efficient identification of these conditions.

methodsWe analyzed data from 3,626 outpatients seen at two hospitals between February 2015 and June 2022. Through manual chart review, we established ground truth labels for the presence or absence of MCI/ADRD diagnoses. Our model combined three types of data: (1) unstructured clinical notes, from which we extracted single words, two-word phrases (bigrams), and three-word phrases (trigrams) as features, weighted using Term Frequency-Inverse Document Frequency (TF-IDF) to capture their relative importance, (2) International Classification of Diseases (ICD) codes, and (3) medication prescriptions related to MCI/ADRD. We trained a regularized logistic regression model to predict MCI/ADRD diagnoses and evaluated its performance using standard metrics including area under the receiver operating curve (AUROC), area under the precision-recall curve (AUPRC), accuracy, specificity, precision, recall, and F1 score.

resultsThirty percent of patients in the cohort carried diagnoses of MCI/ADRD based on manual review. When evaluated on a held-out test set, the best model using clinical notes, ICDs, and medications, achieved an AUROC of 0.98, an AUPRC of 0.98, an accuracy of 0.93, a sensitivity (recall) of 0.91, a specificity of 0.96, a precision of 0.96, and an F1 score of 0.93 The estimated overall accuracy for patients randomly selected from EHRs was 99.88%.

conclusionAutomated EHR phenotyping accurately identifies patients with MCI/ADRD based on clinical notes, ICD codes, and medication records. This approach holds potential for large-scale MCI/ADRD research utilizing EHR databases.

Indexed as

Alzheimer DiseaseCognitive DysfunctionDementiaElectronic Health RecordsAgedAged, 80 and overFemaleHumansMalePhenotypeAlzheimer diseaseDementiaElectronic health recordsMachine learningMild cognitive impairment

Identifiers

PMID40222334
PMCPMC13719700

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.