Natural Language Processing as an Emerging Tool to Detect Late-Life Depression

被引:23
作者
DeSouza, Danielle D. [1 ]
Robin, Jessica [1 ]
Gumus, Melisa [1 ]
Yeung, Anthony [2 ]
机构
[1] Winterlight Labs, Toronto, ON, Canada
[2] Univ Toronto, Dept Psychiat, Toronto, ON, Canada
来源
FRONTIERS IN PSYCHIATRY | 2021年 / 12卷
关键词
geriatric mental health; depression; speech; natural language processing; artificial intelligence; digital health; late-life depression; SPEECH PAUSE TIME; OLDER-ADULTS; PSYCHOMOTOR RETARDATION; ACOUSTIC FEATURES; RATING-SCALE; SEVERITY; DISEASE; VALIDATION; DISORDERS; SYMPTOMS;
D O I
10.3389/fpsyt.2021.719125
中图分类号
R749 [精神病学];
学科分类号
100205 ;
摘要
Late-life depression (LLD) is a major public health concern. Despite the availability of effective treatments for depression, barriers to screening and diagnosis still exist. The use of current standardized depression assessments can lead to underdiagnosis or misdiagnosis due to subjective symptom reporting and the distinct cognitive, psychomotor, and somatic features of LLD. To overcome these limitations, there has been a growing interest in the development of objective measures of depression using artificial intelligence (AI) technologies such as natural language processing (NLP). NLP approaches focus on the analysis of acoustic and linguistic aspects of human language derived from text and speech and can be integrated with machine learning approaches to classify depression and its severity. In this review, we will provide rationale for the use of NLP methods to study depression using speech, summarize previous research using NLP in LLD, compare findings to younger adults with depression and older adults with other clinical conditions, and discuss future directions including the use of complementary AI strategies to fully capture the spectrum of LLD.</p>
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页数:8
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