Suppr超能文献

利用电子健康记录进行认知功能特征描述。

Cognitive Function Characterization Using Electronic Health Records Notes.

机构信息

Department of Biomedical Informatics.

School of Nursing.

出版信息

AMIA Annu Symp Proc. 2022 Feb 21;2021:999-1008. eCollection 2021.

Abstract

Cognitive impairment is a defining feature of neurological disorders such as Alzheimer's disease (AD), one of the leading causes of disability and mortality in the elderly population. Assessing cognitive impairment is important for diagnostic, clinical management, and research purposes. The Folstein Mini-Mental State Examination (MMSE) is the most common screening measure of cognitive function, yet this score is not consistently available in the electronic health records. We conducted a pilot study to extract frequently used concepts characterizing cognitive function from the clinical notes of AD patients in an Aging and Dementia clinical practice. Then we developed a model to infer the severity of cognitive impairment and created a subspecialized taxonomy for concepts associated with MMSE scores. We evaluated the taxonomy and the severity prediction model and presented example use cases of this model.

摘要

认知障碍是神经紊乱的一个显著特征,如阿尔茨海默病(AD),这是老年人群中导致残疾和死亡的主要原因之一。评估认知障碍对于诊断、临床管理和研究目的都很重要。福氏简易精神状态检查(MMSE)是认知功能最常用的筛查测量方法,但该评分在电子健康记录中并不常见。我们进行了一项试点研究,从 AD 患者的临床记录中提取描述认知功能的常用概念。然后,我们开发了一个模型来推断认知障碍的严重程度,并为与 MMSE 评分相关的概念创建了一个专门的分类法。我们评估了分类法和严重程度预测模型,并展示了该模型的示例应用案例。

相似文献

1
Cognitive Function Characterization Using Electronic Health Records Notes.
AMIA Annu Symp Proc. 2022 Feb 21;2021:999-1008. eCollection 2021.
4
Mini Mental State Examination and Logical Memory scores for entry into Alzheimer's disease trials.
Alzheimers Res Ther. 2016 Feb 22;8:9. doi: 10.1186/s13195-016-0176-z.
8
Metrological properties of neuropsychological tests for measuring cognitive change in individuals with prodromal Alzheimer's disease.
Aging Ment Health. 2022 Oct;26(10):1988-1996. doi: 10.1080/13607863.2021.1966746. Epub 2021 Aug 19.

引用本文的文献

本文引用的文献

1
Using Unsupervised Learning to Identify Clinical Subtypes of Alzheimer's Disease in Electronic Health Records.
Stud Health Technol Inform. 2020 Jun 16;270:499-503. doi: 10.3233/SHTI200210.
2
2020 Alzheimer's disease facts and figures.
Alzheimers Dement. 2020 Mar 10. doi: 10.1002/alz.12068.
3
Alzheimer's disease drug development pipeline: 2019.
Alzheimers Dement (N Y). 2019 Jul 9;5:272-293. doi: 10.1016/j.trci.2019.05.008. eCollection 2019.
4
Using electronic health records to estimate the prevalence of agitation in Alzheimer disease/dementia.
Int J Geriatr Psychiatry. 2019 Mar;34(3):420-431. doi: 10.1002/gps.5030. Epub 2018 Dec 27.
6
The Data Gap in the EHR for Clinical Research Eligibility Screening.
AMIA Jt Summits Transl Sci Proc. 2018 May 18;2017:320-329. eCollection 2018.
7
Clinical trials recruitment planning: A proposed framework from the Clinical Trials Transformation Initiative.
Contemp Clin Trials. 2018 Mar;66:74-79. doi: 10.1016/j.cct.2018.01.003. Epub 2018 Jan 9.
8
An Automated Approach to Identifying Patients with Dementia Using Electronic Medical Records.
J Am Geriatr Soc. 2017 Mar;65(3):658-659. doi: 10.1111/jgs.14744. Epub 2017 Feb 2.

文献AI研究员

20分钟写一篇综述,助力文献阅读效率提升50倍。

立即体验

用中文搜PubMed

大模型驱动的PubMed中文搜索引擎

马上搜索

文档翻译

学术文献翻译模型,支持多种主流文档格式。

立即体验