Screening For Depression With Retrospectively Harvested Private Versus Public Text

Tlachac, ML; Rundensteiner, Elke A.

doi:10.1109/jbhi.2020.2983035

Cited by 32 publications

(9 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Diagnosis through a smartphone uses a validated self-report screening tool along with passive monitoring. 12,[41][42][43] This selfreport screening tool is simple, economical, and familiar to people. The Patient Health Questionnaire (PHQ) used in the self-reported depression diagnosis is a depression self-report tool.…”

Section: Introductionmentioning

confidence: 99%

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Kim¹,

Lee²

2021

NDT

View full text Add to dashboard Cite

Section: Introductionmentioning

confidence: 99%

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Kim¹,

Lee²

2021

NDT

View full text Add to dashboard Cite

“…Text is a promising modality for screening depression due to its non-invasiveness, cost-effectiveness, and ability to accumulate records for tracking mental health over a given period. Various types of texts can be leveraged with the help of AI for depression screening, such as social media posts, handwritten notes (either from individuals or discharge summaries from medical professional), and written text transcripts of speech [ 22 – 31 ].…”

Section: Introductionmentioning

confidence: 99%

“…The majority of recent studies have examined the potential for detecting depressive symptoms using informal text of social media posts [ 22 – 26 ]. A study from Wani et al (2022), is among the existing literature that used deep learning, such as convolutional neural network (CNN) and a long-short term memory (LSTM) for depression classification from around 53,000 of social media posts in English language and produced state-of-the-art results with classification accuracy of 99.02% [ 24 ].…”

Section: Introductionmentioning

confidence: 99%

“…Since the majority of information is classified according to the hashtag or content of the posts, it is particularly problematic whether posts are originated from depressed users or are obviously fabricated. This makes the model training on a non-clinically proven dataset [ 22 – 26 , 30 , 31 ]. In contrast, text transcripts from spoken responses are better suited for classifying depression.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression

et al. 2023

View full text Add to dashboard Cite

Depression is a serious mental health disorder that poses a major public health concern in Thailand and have a profound impact on individuals’ physical and mental health. In addition, the lack of number to mental health services and limited number of psychiatrists in Thailand make depression particularly challenging to diagnose and treat, leaving many individuals with the condition untreated. Recent studies have explored the use of natural language processing to enable access to the classification of depression, particularly with a trend toward transfer learning from pre-trained language model. In this study, we attempted to evaluate the effectiveness of using XLM-RoBERTa, a pre-trained multi-lingual language model supporting the Thai language, for the classification of depression from a limited set of text transcripts from speech responses. Twelve Thai depression assessment questions were developed to collect text transcripts of speech responses to be used with XLM-RoBERTa in transfer learning. The results of transfer learning with text transcription from speech responses of 80 participants (40 with depression and 40 normal control) showed that when only one question (Q1) of “How are you these days?” was used, the recall, precision, specificity, and accuracy were 82.5%, 84.65, 85.00, and 83.75%, respectively. When utilizing the first three questions from Thai depression assessment tasks (Q1 − Q3), the values increased to 87.50%, 92.11%, 92.50%, and 90.00%, respectively. The local interpretable model explanations were analyzed to determine which words contributed the most to the model’s word cloud visualization. Our findings were consistent with previously published literature and provide similar explanation for clinical settings. It was discovered that the classification model for individuals with depression relied heavily on negative terms such as ‘not,’ ‘sad,’, ‘mood’, ‘suicide’, ‘bad’, and ‘bore’ whereas normal control participants used neutral to positive terms such as ‘recently,’ ‘fine,’, ‘normally’, ‘work’, and ‘working’. The findings of the study suggest that screening for depression can be facilitated by eliciting just three questions from patients with depression, making the process more accessible and less time-consuming while reducing the already huge burden on healthcare workers.

show abstract

“…While one study involving adolescents (n=13, mean age: 14.93 years) showed a significant negative correlation between depression scores and daily average call duration [34], another study involving adults (n=74, mean age: 44.4 years) showed a significant positive correlation [33]. In the nonclinical population, passive smartphone data has been used to monitor anxiety and depression [21,[36][37][38][39][40][41][42], as well as mental health-related issues, such as stress, well-being, and loneliness [43][44][45][46][47][48][49]. These studies reported a large range of possible features derived from passive smartphone data.…”

Section: Introductionmentioning

confidence: 99%

The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review

Girousse¹,

Vuillerme²

2022

JMIR Res Protoc

View full text Add to dashboard Cite

Background College students are particularly at risk of depression and anxiety. These disorders have a serious impact on public health and affect patients’ daily lives. The potential for using smartphones to monitor these mental conditions, providing passively collected physiological and behavioral data, has been reported among the general population. However, research on the use of passive smartphone data to monitor anxiety and depression among specific populations of college students has never been reviewed. Objective This review’s objectives are (1) to provide an overview of the use of passive smartphone data to monitor depression and anxiety among college students, given their specific type of smartphone use and living setting, and (2) to evaluate the different methods used to assess those smartphone data, including their strengths and limitations. Methods This review will follow the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) guidelines. Two independent investigators will review English-language, full-text, peer-reviewed papers extracted from PubMed and Web of Science that measure passive smartphone data and levels of depression or anxiety among college students. A preliminary search was conducted in February 2022 as a proof of concept. Results Our preliminary search identified 115 original articles, 8 of which met our eligibility criteria. Our planned full study will include an article selection flowchart, tables, and figures representing the main information extracted on the use of passive smartphone data to monitor anxiety and depression among college students. Conclusions The planned review will summarize the published research on using passive smartphone data to monitor anxiety and depression among college students. The review aims to better understand whether and how passive smartphone data are associated with indicators of depression and anxiety among college students. This could be valuable in order to provide a digital solution for monitoring mental health issues in this specific population by enabling easier identification and follow-up of the patients. Trial Registration PROSPERO CRD42022316263; https://www.crd.york.ac.uk/prospero/display_record.php?RecordID=316263 International Registered Report Identifier (IRRID) DERR1-10.2196/38785

show abstract

Screening For Depression With Retrospectively Harvested Private Versus Public Text

Cited by 32 publications

References 16 publications

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Screening for Depression in Mobile Devices Using Patient Health Questionnaire-9 (PHQ-9) Data: A Diagnostic Meta-Analysis via Machine Learning Methods

Classification and analysis of text transcription from Thai depression assessment tasks among patients with depression

The Use of Passive Smartphone Data to Monitor Anxiety and Depression Among College Students in Real-World Settings: Protocol for a Systematic Review

Contact Info

Product

Resources

About