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

IF 5 Q1 GERIATRICS & GERONTOLOGY
JMIR Aging Pub Date : 2025-02-25 DOI:10.2196/65221
Hunki Paek, Richard H Fortinsky, Kyeryoung Lee, Liang-Chin Huang, Yazeed S Maghaydah, George A Kuchel, Xiaoyan Wang
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引用次数: 0

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.

自然语言处理揭示痴呆诊断轨迹和临床实践模式的真实世界洞察:发展和可用性研究。
背景:了解痴呆的发展轨迹和门诊的临床实践模式对于有效的管理是至关重要的。从最初的记忆丧失投诉到痴呆症诊断的途径的知识仍然有限。目的:本研究旨在(1)确定门诊患者最初的记忆丧失主诉与痴呆诊断之间的时间间隔,(2)评估痴呆诊断前接受认知增强药物治疗的患者比例,以及(3)确定影响记忆主诉与诊断之间的时间以及认知增强药物处方的患者和提供者特征。方法:本回顾性队列研究使用康涅狄格大学健康中心的大型门诊电子健康记录(EHR)数据库,涵盖2010-2018年,共有581名门诊患者。我们使用定制的基于深度学习的自然语言处理(NLP)管道从电子病历数据中提取临床信息,重点关注认知相关症状、主要照顾者关系和药物使用情况。我们应用描述性统计、线性和逻辑回归进行分析。结果:NLP流水线的准确率、召回率和f1得分分别为0.97、0.93和0.95。从首次记忆丧失主诉到痴呆诊断的中位时间为342天(IQR 200-675)。诸如初始投诉和诊断的位置以及主要照顾者关系等因素显著影响这一间隔。约25.1%(146/581)的患者在诊断前接受了认知增强药物治疗,主诉数量影响药物使用。结论:我们的nlp指导分析提供了从记忆抱怨到痴呆诊断和用药实践的临床途径,可以提高门诊患者的护理和决策。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
JMIR Aging
JMIR Aging Social Sciences-Health (social science)
CiteScore
6.50
自引率
4.10%
发文量
71
审稿时长
12 weeks
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