Cross-Demographic Portability of Deep NLP-Based Depression Models

Rutowski, Tomek; Shriberg, Elizabeth; Harati, Amir; Lü, Yang; Oliveira, Ricardo Augusto Rabelo; Chlebek, Piotr

doi:10.1109/slt48900.2021.9383609

Cited by 9 publications

(9 citation statements)

References 27 publications

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“…In previous research, Ellipsis Health introduced a General Population model based solely on semantic analysis of depression, built upon a younger age distribution with little overlap from the current study population. The model maintained performance when applied to senior populations such as the one examined in this study ( Rutowski et al, 2021 ). Though Ellipsis Health has not reported on the portability of its improved transformer architecture to different populations, these previous results suggest portability across age groups without the need for significant retraining.…”

Section: Introductionmentioning

confidence: 79%

“…Though compensation as in this study may not reflect real-world use, incentives to influence subject behaviors are not uncommon in the clinical practice ( Soliño-Fernandez et al, 2019 ; Vlaev et al, 2019 ). Our training dataset contains mixed ethnicities, but is mostly Caucasian ( Rutowski et al, 2021 ), which does not reflect the country’s population. Further studies should address this discrepancy, especially since there are indications that treatment responses are similar ( Lesser et al, 2011 ).…”

Section: Discussionmentioning

confidence: 99%

“…The ASR technology accounts for issues such as ambient noise, accents, and stuttering, while the NLP algorithm is trained to be equally reliable across speakers of different ages, races, dialectal regions, etc. ( Rutowski et al, 2021 ). As with all technology that analyzes speech, disorders or conditions that affect the ability to produce spoken language may impact the results.…”

Section: Methodsmentioning

confidence: 99%

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Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population

Lin

Nazreen²,

Rutowski³

et al. 2022

Front. Psychol.

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BackgroundDepression and anxiety create a large health burden and increase the risk of premature mortality. Mental health screening is vital, but more sophisticated screening and monitoring methods are needed. The Ellipsis Health App addresses this need by using semantic information from recorded speech to screen for depression and anxiety.ObjectivesThe primary aim of this study is to determine the feasibility of collecting weekly voice samples for mental health screening. Additionally, we aim to demonstrate portability and improved performance of Ellipsis’ machine learning models for patients of various ages.MethodsStudy participants were current patients at Desert Oasis Healthcare, mean age 63 years (SD = 10.3). Two non-randomized cohorts participated: one with a documented history of depression within 24 months prior to the study (Group Positive), and the other without depression (Group Negative). Participants recorded 5-min voice samples weekly for 6 weeks via the Ellipsis Health App. They also completed PHQ-8 and GAD-7 questionnaires to assess for depression and anxiety, respectively.ResultsProtocol completion rate was 61% for both groups. Use beyond protocol was 27% for Group Positive and 9% for Group Negative. The Ellipsis Health App showed an AUC of 0.82 for the combined groups when compared to the PHQ-8 and GAD-7 with a threshold score of 10. Performance was high for senior participants as well as younger age ranges. Additionally, many participants spoke longer than the required 5 min.ConclusionThe Ellipsis Health App demonstrated feasibility in using voice recordings to screen for depression and anxiety among various age groups and the machine learning models using Transformer methodology maintain performance and improve over LSTM methodology when applied to the study population.

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Section: Introductionmentioning

confidence: 79%

Section: Discussionmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population

Lin

Nazreen²,

Rutowski³

et al. 2022

Front. Psychol.

Self Cite

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“…Another key challenge remains is the lack of large labeled datasets for evaluating performance across various methods. To this end, it is encouraging to note that recent efforts backed by companies like Ellipsis Health [163], [184], have used deep learning and transfer learning to predict depression and anxiety scores with a high performance based on a large labelled dataset of over 10,000 unique speakers. Some commercial applications claim to have human-level accuracy in detecting depression using only 20-30 seconds of audio clip [185], [186].…”

Section: Speech and Video Analysesmentioning

confidence: 99%

Modern Views of Machine Learning for Precision Psychiatry

Chen¹,

Prathamesh²,

Kulkarni³

et al. 2022

Preprint

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In light of the NIMH’s Research Domain Criteria (RDoC), the advent of functional neuroimaging, novel technologies and methods provide new opportunities to develop precise and personalized prognosis and diagnosis of mental disorders. Machine learning (ML) and artificial intelligence (AI) technologies are playing an increasingly critical role in the new era of precision psychiatry. Combining ML/AI with neuromodulation technologies can potentially provide explainable solutions in clinical practice and effective therapeutic treatment. Advanced wearable and mobile technologies also call for the new role of ML/AI for digital phenotyping in mobile mental health. In this review, we provide a comprehensive review of the ML methodologies and applications by combining neuroimaging, neuromodulation and advanced mobile technologies in psychiatry practice. Additionally, we review the role of ML in molecular phenotyping, cross-species biomarker identification in precision psychiatry. We further discuss explainable AI (XAI) and causality testing in a closed-human-in-the-loop manner, and highlight the ML potential in multimedia information extraction and multimodal data fusion. Finally, we discuss conceptual and practical challenges in precision psychiatry and highlight ML opportunities in future research.

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“…In addition, the platform offers an oncologist-facing dashboard, which facilitates patient referral for psycho-oncology services and allows timely coordination of care and patient-/person-centered approaches. Finally, Ellipsis Health has published a series of peer-reviewed technical papers validating the machine learning algorithms as well as the speech recognition performance that power the approach [ 39 , 40 , 41 , 42 , 43 ].…”

Section: Introductionmentioning

confidence: 99%

Evaluating the Feasibility and Acceptability of an Artificial-Intelligence-Enabled and Speech-Based Distress Screening Mobile App for Adolescents and Young Adults Diagnosed with Cancer: A Study Protocol

Zhang

Acquati

Aratow

et al. 2022

Cancers

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Adolescents and young adults (AYAs) diagnosed with cancer are an age-defined population, with studies reporting up to 45% of the population experiencing psychological distress. Although it is essential to screen and monitor for psychological distress throughout AYAs’ cancer journeys, many cancer centers fail to effectively implement distress screening protocols largely due to busy clinical workflow and survey fatigue. Recent advances in mobile technology and speech science have enabled flexible and engaging methods to monitor psychological distress. However, patient-centered research focusing on these methods’ feasibility and acceptability remains lacking. Therefore, in this project, we aim to evaluate the feasibility and acceptability of an artificial intelligence (AI)-enabled and speech-based mobile application to monitor psychological distress among AYAs diagnosed with cancer. We use a single-arm prospective cohort design with a stratified sampling strategy. We aim to recruit 60 AYAs diagnosed with cancer and to monitor their psychological distress using an AI-enabled speech-based distress monitoring tool over a 6 month period. The primary feasibility endpoint of this study is defined by the number of participants completing four out of six monthly distress assessments, and the acceptability endpoint is defined both quantitatively using the acceptability of intervention measure and qualitatively using semi-structured interviews.

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Cross-Demographic Portability of Deep NLP-Based Depression Models

Cited by 9 publications

References 27 publications

Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population

Feasibility of a Machine Learning-Based Smartphone Application in Detecting Depression and Anxiety in a Generally Senior Population

Modern Views of Machine Learning for Precision Psychiatry

Evaluating the Feasibility and Acceptability of an Artificial-Intelligence-Enabled and Speech-Based Distress Screening Mobile App for Adolescents and Young Adults Diagnosed with Cancer: A Study Protocol

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