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

Abstract

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.

Generative Pre-Trained Transformer-Empowered Healthcare Conversations: Current Trends, Challenges, and Future Directions in Large Language Model-Enabled Medical Chatbots

“…Moreover, it is possible for the LLM to integrate with telehealth services especially for those who have a disability [53]. In addition, it is believed that LLMs are even able to detect later-life depression, which is a kind of major public health concern that occurs in the older generation, as the name states [65]. NLP analyzes the way people talk and also the speed and pitch they use in order to understand their speech patterns [65].…”

Quantifying the Processes and Events of Psychotherapy at Scale

“…Recent studies in patients with mood disorders have demonstrated that a number of acoustic measures characterized by source features from the vocal folds (e.g., jitter, shimmer, harmonics-to-noise ratio (HNR), lter features from the vocal tract (e.g., F1 and F2 formants), spectral features (e.g., Mel Frequency Cepstral Coe cients (MFCCs), and prosodic/melodic features (e.g., fundamental frequency (F0)), speech intensity, speed, and pause duration) have been shown to be altered in individuals with depression (33).…”

“…The majority of clinically relevant studies are unfortunately based on smaller and less representative data samples. These are usually directly used for analysis and become unsuitable for training a new generation of models (33).…”

Natural Language Processing: from Bedside to Everywhere

“…In psychiatry, NLP can be used for IE from unstructured EHR and speech analysis on patient speech data [63,64]. NLP can help in the screening, early diagnosis, or severity estimation of various diseases such as depression [63], bipolar disorder [65], dementia [66][67][68], psychosis [69,70], and schizophrenia [71]. Dai…”