ArticleContemporary clinical trials communications2020
The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.
Article in Contemporary clinical trials communications, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Non-verbal behaviours as predictors for treatment response in patients with depression or schizophrenia: a systematic review.Frontiers in psychiatry · 2025Pooled it
- Facial phenotypes in Alzheimer's disease: from neurobiology to artificial intelligence.Alzheimer's research & therapy · 2026Review
- Toward a hybrid assessment framework for adolescent borderline personality disorder: a mini review of personality functioning, digital biomarkers, and AI-supported assessment.Frontiers in psychiatry · 2026Review
- AI-Based Facial Emotion Analysis for Early and Differential Diagnosis of Dementia.Bioengineering (Basel, Switzerland) · 2025Article
- Increasing the Construct Validity of Computational Phenotypes of Mental Illness Through Active Inference and Brain Imaging.Brain sciences · 2024Review
- Detecting Dementia from Face-Related Features with Automated Computational Methods.Bioengineering (Basel, Switzerland) · 2023Article
- Language patterns in Japanese patients with Alzheimer disease: A machine learning approach.Psychiatry and clinical neurosciences · 2023Article
- Intelligent speech technologies for transcription, disease diagnosis, and medical equipment interactive control in smart hospitals: A review.Computers in biology and medicine · 2023Review
- Identifying neurocognitive disorder using vector representation of free conversation.Scientific reports · 2022Article
- Unobtrusive Sensing Technology for Quantifying Stress and Well-Being Using Pulse, Speech, Body Motion, and Electrodermal Data in a Workplace Setting: Study Concept and Design.Frontiers in psychiatry · 2021Article
- Speech Quality Feature Analysis for Classification of Depression and Dementia Patients.Sensors (Basel, Switzerland) · 2020Article
- Article
- Using speech recognition technology to investigate the association between timing-related speech features and depression severity.PloS one · 2020Article
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Authors and funding
24 authors.
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Abstract
introductionDepressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep.
methodsMajor depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms. DISCUSSION: The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity.
trial registrationUMIN000021396, University Hospital Medical Information Network (UMIN).
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