TY - JOUR
T1 - The project for objective measures using computational psychiatry technology (PROMPT)
T2 - Rationale, design, and methodology
AU - PROMPT collaborators
AU - Kishimoto, Taishiro
AU - Takamiya, Akihiro
AU - Liang, Kuo ching
AU - Funaki, Kei
AU - Fujita, Takanori
AU - Kitazawa, Momoko
AU - Yoshimura, Michitaka
AU - Tazawa, Yuki
AU - Horigome, Toshiro
AU - Eguchi, Yoko
AU - Kikuchi, Toshiaki
AU - Tomita, Masayuki
AU - Bun, Shogyoku
AU - Murakami, Junichi
AU - Sumali, Brian
AU - Warnita, Tifani
AU - Kishi, Aiko
AU - Yotsui, Mizuki
AU - Toyoshiba, Hiroyoshi
AU - Mitsukura, Yasue
AU - Shinoda, Koichi
AU - Sakakibara, Yasubumi
AU - Mimura, Masaru
N1 - Funding Information:
This research is supported by the Japan Agency for Medical Research and Development (AMED) under Grant Number JP18he1102004 . The Grant was awarded on Oct. 29, 2015 and ends on Mar. 31, 2019. The funding source did not participate in the design of this study and will not have any hand in the study's execution, analyses, or submission of results.
Funding Information:
The Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), which is funded by the Japan Agency for Medical Research and Development (AMED), is an industry-academia collaborative research project that aims to develop new techniques for diagnosing and evaluating illness severity utilizing the technology described above, with the hope that this research will prove useful in every-day clinical settings and trials.
Publisher Copyright:
© 2020 The Authors
PY - 2020/9
Y1 - 2020/9
N2 - Introduction: Depressive 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. Methods: Major 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 Registration: UMIN000021396, University Hospital Medical Information Network (UMIN).
AB - Introduction: Depressive 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. Methods: Major 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 Registration: UMIN000021396, University Hospital Medical Information Network (UMIN).
KW - Depression
KW - Machine learning
KW - Natural language processing
KW - Neurocognitive disorder
KW - Screening
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U2 - 10.1016/j.conctc.2020.100649
DO - 10.1016/j.conctc.2020.100649
M3 - Article
AN - SCOPUS:85089797478
VL - 19
JO - Contemporary Clinical Trials Communications
JF - Contemporary Clinical Trials Communications
SN - 2451-8654
M1 - 100649
ER -