Evidence map›Paper›PMID 39999185›Full record

ArticleJMIR aging2025

Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study.

Hunki Paek, Richard H Fortinsky, Kyeryoung Lee, Liang-Chin Huang, Yazeed S Maghaydah, George A Kuchel, Xiaoyan Wang

Abstract read
In one paragraph

Article in JMIR aging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Hunki Paek *IMO Health, Rosemont, IL, United States.ORCID 0009-0000-9916-5654
Richard H Fortinsky *UConn Center on Aging, University of Connecticut School of Medicine, Farmington, CT, United States.ORCID 0000-0002-2013-719X
Kyeryoung LeeIMO Health, Rosemont, IL, United States.ORCID 0000-0002-6937-9931
Liang-Chin HuangIMO Health, Rosemont, IL, United States.ORCID 0000-0001-5661-8940
Yazeed S MaghaydahUConn Center on Aging, University of Connecticut School of Medicine, Farmington, CT, United States.ORCID 0000-0002-0842-3265
George A KuchelUConn Center on Aging, University of Connecticut School of Medicine, Farmington, CT, United States.ORCID 0000-0001-8387-7040
Xiaoyan WangCenter for Quantitative Medicine, University of Connecticut School of Medicine, 195 Farmington Ave, Farmington, CT, 06032, United States, 1 201-282-8098.ORCID 0000-0002-4193-4120

Funding

Research Education ComponentP30AG067988 · NIA · UNIVERSITY OF CONNECTICUT SCH OF MED/DNT · PI Richard H Fortinsky, GEORGE A KUCHEL · 2021 to 2026
$9.5M
NIA NIH HHS P30 AG067988
6 · The paper itself

Abstract

Background: Understanding the dementia disease trajectory and clinical practice patterns in outpatient settings is vital for effective management. Knowledge about the path from initial memory loss complaints to dementia diagnosis remains limited. Objective: This study aims to (1) determine the time intervals between initial memory loss complaints and dementia diagnosis in outpatient care, (2) assess the proportion of patients receiving cognition-enhancing medication prior to dementia diagnosis, and (3) identify patient and provider characteristics that influence the time between memory complaints and diagnosis and the prescription of cognition-enhancing medication. Methods: This retrospective cohort study used a large outpatient electronic health record (EHR) database from the University of Connecticut Health Center, covering 2010-2018, with a cohort of 581 outpatients. We used a customized deep learning-based natural language processing (NLP) pipeline to extract clinical information from EHR data, focusing on cognition-related symptoms, primary caregiver relation, and medication usage. We applied descriptive statistics, linear, and logistic regression for analysis. Results: The NLP pipeline showed precision, recall, and F1-scores of 0.97, 0.93, and 0.95, respectively. The median time from the first memory loss complaint to dementia diagnosis was 342 (IQR 200-675) days. Factors such as the location of initial complaints and diagnosis and primary caregiver relationships significantly affected this interval. Around 25.1% (146/581) of patients were prescribed cognition-enhancing medication before diagnosis, with the number of complaints influencing medication usage. Conclusions: Our NLP-guided analysis provided insights into the clinical pathways from memory complaints to dementia diagnosis and medication practices, which can enhance patient care and decision-making in outpatient settings.

Indexed as

DementiaMemory DisordersNatural Language ProcessingPractice Patterns, Physicians'AgedAged, 80 and overDisease ProgressionElectronic Health RecordsFemaleHumansMaleRetrospective StudiesagingAlzheimer diseasecognitivecohortdeep learningdementiadiagnosisdiagnosticEHRelectronic health recordsgeriatricmachine learningmemorymemory lossnatural language processingNLPolder adultspatternprognosisreal-world insightstrajectory

Identifiers

PMID39999185
PMCPMC11878476

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Registered trials

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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.