Rapid Anxiety and Depression Diagnosis in Young Children Enabled by Wearable Sensors and Machine Learning

McGinnis, Ryan S.; McGinnis, Ellen; Hruschak, Jessica; Lopez-Duran, Nestor L.; Fitzgerald, Kate D.; Rosenblum, Katherine L.; Muzik, Maria

doi:10.1109/embc.2018.8513327

Cited by 27 publications

(14 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Screening for major depression in the mobile environment has been carried out in the general population (eg, university students, college students, the general community 12,30,42,52,53,107,108 ), clinical field, and primary care field. 104,[109][110][111] Furthermore, depression diagnoses are performed for those examined in previous studies 32,112,113 and participants recruited through an app. 114,115 The strength of depression screening in a mobile environment is that the group is not limited to one environment; therefore, more data can be obtained.…”

mentioning

confidence: 99%

See 1 more Smart Citation

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

mentioning

confidence: 99%

“…For the diagnosis of depression and mood disorders, machine learning with excellent predictive suitability has been introduced. 57,110,113,116,117 Datasets collected in mobile settings are large; machine learning-based predictive models can analyze a large amount of data. This technique is useful for analyzing and conceptualizing multiple predictors.…”

mentioning

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

“…These stages were achieved by gradually exposing the participants to realistic-looking rubber snakes. Wearable sensors recorded motion, which was then analyzed by an ML-based algorithm that predicted participant classification to a disorder or a control group [ 27 ]. The algorithm exhibited 80% diagnostic accuracy.…”

Section: Laboratory-based Assessmentsmentioning

confidence: 99%

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

Richter

Fishbain

Richter‐Levin

et al. 2021

JPM

View full text Add to dashboard Cite

The psychiatric diagnostic procedure is currently based on self-reports that are subject to personal biases. Therefore, the diagnostic process would benefit greatly from data-driven tools that can enhance accuracy and specificity. In recent years, many studies have achieved promising results in detecting and diagnosing depression based on machine learning (ML) analysis. Despite these favorable results in depression diagnosis, which are primarily based on ML analysis of neuroimaging data, most patients do not have access to neuroimaging tools. Hence, objective assessment tools are needed that can be easily integrated into the routine psychiatric diagnostic process. One solution is to use behavioral data, which can be easily collected while still maintaining objectivity. The current paper summarizes the main ML-based approaches that use behavioral data in diagnosing depression and other psychiatric disorders. We classified these studies into two main categories: (a) laboratory-based assessments and (b) data mining, the latter of which we further divided into two sub-groups: (i) social media usage and movement sensors data and (ii) demographic and clinical information. The paper discusses the advantages and challenges in this field and suggests future research directions and implementations. The paper’s overarching aim is to serve as a first step in synthetizing existing knowledge about ML-based behavioral diagnosis studies in order to develop interventions and individually tailored treatments in the future.

show abstract

“…Less research has focused on integrating sensor technology into mental health research and clinical care, although this area is beginning to grow. For example, sensor technology has been applied to the assessment of pediatric anxiety and depression, allowing researchers to diagnose childhood internalizing disorders with 80% accuracy, which is comparable to the accuracy of using structured clinical interviews, but is much less time consuming (McGinnis et al, ). These advances may change the standard practice for clinical assessment, leading to increased accuracy and decreased cost and time investment.…”

Section: Sensor Technology In Mental Healthmentioning

confidence: 99%

Sensor technology implementation for research, treatment, and assessment of eating disorders

Levinson

Christian

Shankar‐Ram

et al. 2019

Intl J Eating Disorders

View full text Add to dashboard Cite

Sensor technology has made huge technological advances in the past decade. Many sensor technologies (e.g., wearable wristbands) have been integrated into health research with the ability to substantially improve health outcomes and reduce health care costs. Despite the rapid technological developments in sensor technology, little research has examined sensor technology in eating disorders (EDs). The overarching aim of the current article is to briefly review the literature on sensor technology and health outcomes, including EDs, and discuss several potential ideas for the application of sensor technology in the treatment, assessment, and diagnosis of EDs. We will also present data from a feasibility case study with an ED participant and healthy control providing a brief example of how wearable sensor technology might be implemented in ED research. Overall, we will discuss how sensor technology could be used to improve treatment and assessment of EDs and represents an idea in need of more research in the ED field.

show abstract

Rapid Anxiety and Depression Diagnosis in Young Children Enabled by Wearable Sensors and Machine Learning

Cited by 27 publications

References 24 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

Machine Learning-Based Behavioral Diagnostic Tools for Depression: Advances, Challenges, and Future Directions

Sensor technology implementation for research, treatment, and assessment of eating disorders

Contact Info

Product

Resources

About