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家庭医疗中痴呆症状的自由文本记录:一项自然语言处理研究。

Free-Text Documentation of Dementia Symptoms in Home Healthcare: A Natural Language Processing Study.

作者信息

Topaz Maxim, Adams Victoria, Wilson Paula, Woo Kyungmi, Ryvicker Miriam

机构信息

Columbia University, New York, NY, USA.

Visiting Nurse Service of New York, New York, NY, USA.

出版信息

Gerontol Geriatr Med. 2020 Sep 24;6:2333721420959861. doi: 10.1177/2333721420959861. eCollection 2020 Jan-Dec.

DOI:10.1177/2333721420959861
PMID:33029550
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC7520927/
Abstract

BACKGROUND

Little is known about symptom documentation related to Alzheimer's disease and related dementias (ADRD) by home healthcare (HHC) clinicians.

OBJECTIVE

This study: (1) developed a natural language processing (NLP) algorithm that identifies common neuropsychiatric symptoms of ADRD in HHC free-text clinical notes; (2) described symptom clusters and hospitalization or emergency department (ED) visit rates for patients with and without these symptoms.

METHOD

We examined a corpus of -2.6 million free-text notes for 112,237 HHC episodes among 89,459 patients admitted to a non-profit HHC agency for post-acute care with any diagnosis. We used NLP software (NimbleMiner) to construct indicators of six neuropsychiatric symptoms. Structured HHC assessment data were used to identify known ADRD diagnoses and construct measures of hospitalization/ED use during HHC.

RESULTS

Neuropsychiatric symptoms were documented for 40% of episodes. Common clusters included impaired memory, anxiety and/or depressed mood. One in three episodes without an ADRD diagnosis had documented symptoms. Hospitalization/ED rates increased with one or more symptoms present.

CONCLUSION

HHC providers should examine episodes with neuropsychiatric symptoms but no ADRD diagnoses to determine whether ADRD diagnosis was missed or to recommend ADRD evaluation. NLP-generated symptom indicators can help to identify high-risk patients for targeted interventions.

摘要

背景

关于家庭医疗保健(HHC)临床医生对阿尔茨海默病及相关痴呆症(ADRD)症状的记录情况,目前所知甚少。

目的

本研究:(1)开发一种自然语言处理(NLP)算法,用于识别HHC自由文本临床记录中ADRD的常见神经精神症状;(2)描述有和没有这些症状的患者的症状群以及住院或急诊就诊率。

方法

我们检查了一家非营利性HHC机构收治的89459例接受急性后护理且有任何诊断的患者中112237次HHC事件的约260万条自由文本记录。我们使用NLP软件(NimbleMiner)构建六种神经精神症状的指标。结构化的HHC评估数据用于识别已知的ADRD诊断,并构建HHC期间住院/急诊使用情况的测量指标。

结果

40%的事件记录了神经精神症状。常见的症状群包括记忆受损、焦虑和/或情绪低落。三分之一没有ADRD诊断记录的事件有症状记录。出现一种或多种症状时,住院/急诊率会增加。

结论

HHC提供者应检查有神经精神症状但无ADRD诊断的事件,以确定是否漏诊ADRD或建议进行ADRD评估。NLP生成的症状指标有助于识别需要进行针对性干预的高危患者。

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