The Project for Objective Measures Using Computational Psychiatry Technology (PROMPT): Rationale, design, and methodology

Taishiro Kishimoto, Akihiro Takamiya, Kuo Ching Liang, Kei Funaki, Takanori Fujita, Momoko Kitazawa, Michitaka Yoshimura, Yuki Tazawa, Toshiro Horigome, Yoko Eguchi, Toshiaki Kikuchi, Masayuki Tomita, Shogyoku Bun, Junichi Murakami, Brian Sumali, Tifani Warnita, Aiko Kishi, Mizuki Yotsui, Hiroyoshi Toyoshiba, Yasue MitsukuraKoichi Shinoda, Yasubumi Sakakibara, Masaru Mimura

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. Overcoming these disorders is an extremely important public health problem today. 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. Due to advances in technology, it has become possible to quantify important features that clinicians perceive as reflective of disorder severity. Such features include facial expressions, phonic/speech information, body motion, daily activity, and sleep. 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. Methods: This is a multi-center prospective study. DSM-5 criteria for major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders are inclusion criteria for the depressive and neurocognitive disorder samples. Healthy samples are confirmed to have no history of psychiatric disorders by Mini-International Neuropsychiatric Interview, and have no current cognitive decline based on the Mini Mental State Examination. Participants go through approximately 10-minute interviews with a psychiatrist/psychologist, where participants talk about non-specific topics such as everyday living, symptoms of disease, hobbies, etc. 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. The interviews take place ≤10 times within up to five years of followup. 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 PROMPT goal is to develop objective digital 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).

Original languageEnglish
JournalUnknown Journal
DOIs
Publication statusPublished - 2019 Nov 29

Keywords

  • Body motion
  • Depression
  • Facial expression
  • Machine learning
  • Natural language processing
  • Neurocognitive disorder
  • Screening
  • Severity
  • Speech
  • Treatment response

ASJC Scopus subject areas

  • Medicine(all)

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