Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study
Abstract BackgroundUnderstanding 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. ObjectiveTh...
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| Format: | Article |
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JMIR Publications
2025-02-01
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| Series: | JMIR Aging |
| Online Access: | https://aging.jmir.org/2025/1/e65221 |
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| author | Hunki Paek Richard H Fortinsky Kyeryoung Lee Liang-Chin Huang Yazeed S Maghaydah George A Kuchel Xiaoyan Wang |
| author_facet | Hunki Paek Richard H Fortinsky Kyeryoung Lee Liang-Chin Huang Yazeed S Maghaydah George A Kuchel Xiaoyan Wang |
| author_sort | Hunki Paek |
| collection | DOAJ |
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Abstract
BackgroundUnderstanding 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.
ObjectiveThis 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.
MethodsThis 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.
ResultsThe NLP pipeline showed precision, recall, and F1
ConclusionsOur 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. |
| format | Article |
| id | doaj-art-0d8e75fa9c7d4626b72eb082b231a1f9 |
| institution | DOAJ |
| issn | 2561-7605 |
| language | English |
| publishDate | 2025-02-01 |
| publisher | JMIR Publications |
| record_format | Article |
| series | JMIR Aging |
| spelling | doaj-art-0d8e75fa9c7d4626b72eb082b231a1f92025-08-20T03:00:21ZengJMIR PublicationsJMIR Aging2561-76052025-02-018e65221e6522110.2196/65221Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability StudyHunki Paekhttp://orcid.org/0009-0000-9916-5654Richard H Fortinskyhttp://orcid.org/0000-0002-2013-719XKyeryoung Leehttp://orcid.org/0000-0002-6937-9931Liang-Chin Huanghttp://orcid.org/0000-0001-5661-8940Yazeed S Maghaydahhttp://orcid.org/0000-0002-0842-3265George A Kuchelhttp://orcid.org/0000-0001-8387-7040Xiaoyan Wanghttp://orcid.org/0000-0002-4193-4120 Abstract BackgroundUnderstanding 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. ObjectiveThis 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. MethodsThis 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. ResultsThe NLP pipeline showed precision, recall, and F1 ConclusionsOur 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.https://aging.jmir.org/2025/1/e65221 |
| spellingShingle | Hunki Paek Richard H Fortinsky Kyeryoung Lee Liang-Chin Huang Yazeed S Maghaydah George A Kuchel Xiaoyan Wang Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study JMIR Aging |
| title | Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study |
| title_full | Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study |
| title_fullStr | Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study |
| title_full_unstemmed | Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study |
| title_short | Real-World Insights Into Dementia Diagnosis Trajectory and Clinical Practice Patterns Unveiled by Natural Language Processing: Development and Usability Study |
| title_sort | real world insights into dementia diagnosis trajectory and clinical practice patterns unveiled by natural language processing development and usability study |
| url | https://aging.jmir.org/2025/1/e65221 |
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