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自然语言处理揭示的痴呆症诊断轨迹和临床实践模式的真实世界见解:开发与可用性研究

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

作者信息

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

机构信息

IMO Health, Rosemont, IL, United States.

UConn Center on Aging, University of Connecticut School of Medicine, Farmington, CT, United States.

出版信息

JMIR Aging. 2025 Feb 25;8:e65221. doi: 10.2196/65221.

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.

摘要

背景

了解痴呆症疾病轨迹和门诊环境中的临床实践模式对于有效管理至关重要。从最初的记忆丧失主诉到痴呆症诊断的路径相关知识仍然有限。

目的

本研究旨在(1)确定门诊护理中从最初记忆丧失主诉到痴呆症诊断的时间间隔,(2)评估痴呆症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

这项回顾性队列研究使用了康涅狄格大学健康中心2010 - 2018年的大型门诊电子健康记录(EHR)数据库,队列中有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EHR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管道的精确率、召回率和F1分数分别为0.97、0.93和0.95。从首次记忆丧失主诉到痴呆症诊断的中位时间为342(四分位间距200 - 675)天。初始主诉和诊断的地点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)的患者在诊断前被开具了认知增强药物,主诉数量影响药物使用情况。

结论

我们的NLP引导分析为从记忆主诉到痴呆症诊断的临床路径和用药实践提供了见解,这可以改善门诊环境中的患者护理和决策。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/0cf0/11878476/f2d75c555f80/aging-v8-e65221-g001.jpg

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