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The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and

Taishiro Kishimoto¹, Akihiro Takamiya¹, Kuo-Ching Liang¹

¹Department of Neuropsychiatry, Keio University School of Medicine, 35 Shinanomachi, Shinjuku, Tokyo, 160-8582, Japan.

Contemporary Clinical Trials Communications

|September 11, 2020

Summary

This summary is machine-generated.

This study aims to develop objective digital biomarkers for assessing depressive and neurocognitive disorders. Computational psychiatry and machine learning will analyze speech, motion, and activity data to improve diagnosis and treatment monitoring.

Keywords:

AMED, Japan Agency for Medical Research and Development Adabag, Adaptive Bagging Adaboost, Adaptive Boosting BD, Bipolar disorder BDI-II, Beck Depression Inventory, Second Edition BNN, Bayesian Neural Networks CDR, Clinical Dementia Rating CDT, Clock Drawing Test CNN, Convolutional Neural Networks CPP, cepstral peak prominence DSM-5, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition Depression F0, fundamental frequency F1, F2, F3, first, second, and third formant frequencies FedRAMP, Federal Risk and Authorization Management Program GCNN, Gated Convolutional Neural Networks GDS, Geriatric Depression Scale HAM-D, Hamilton Depression Rating Scale IEC, International Electrotechnical Commission ISO, International Organization for Standardization LM, Wechsler Memory Scale-Revised Logical Memory LSTM, Long Short-Term Memory Networks M.I.N.I., Mini-International Neuropsychiatric Interview MADRS, Montgomery-Asberg Depression Rating Scale MARS, Motor Agitation and Retardation Scale MCI, mild cognitive impairment MDD, Major depressive disorder MFCC, mel-frequency cepstrum coefficients MMSE, Mini-Mental State Examination MRI, magnetic resonance imaging Machine learning MoCA, Montreal Cognitive Assessment NPI, Neuropsychiatric Inventory Natural language processing Neurocognitive disorder PET, positron emission tomography PROMPT, Project for Objective Measures Using Computational Psychiatry Technology PSQI, Pittsburgh Sleep Quality Index RF, Random Forest RGB, red, green, blue SCID, Structural Clinical Interview for DSM-5 SVM, Support Vector Machine SVR, Support Vector Regression Screening UI, uncertainty interval UMIN, University Hospital Medical Information Network UV, ultraviolet YLDs, years lived with disability YMRS, Young Mania Rating Scale

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Area of Science:

Computational psychiatry
Digital biomarkers
Machine learning in mental health

Background:

Depressive and neurocognitive disorders are leading causes of global disability.
Lack of objective biomarkers hinders treatment assessment and drug development.
New technologies enable quantification of clinically relevant behavioral features.

Purpose of the Study:

To develop objective, noninvasive, and easy-to-use biomarkers for assessing depressive and neurocognitive disorders.
To guide clinical decision-making and reduce clinical trial failure rates.
To leverage computational psychiatry and machine learning for objective mental health assessment.

Main Methods:

Recruitment of patients with major depressive disorder, bipolar disorder, neurocognitive disorders, and healthy controls.
Conducting 10-minute conversational interviews recorded via RGB/infrared cameras and microphones.
Utilizing wearable devices and advanced software for data processing and machine learning analysis.

Main Results:

Machine learning models are employed to predict symptom presence, severity, and changes over time.
Analysis of multimodal data including video, audio, and wearable sensor information.
Focus on extracting features that reflect disorder severity across diverse patient samples.

Conclusions:

The Project for Objective Measures Using Computational Psychiatry Technology (PROMPT) aims to create practical biomarkers.
Objective measures can improve clinical practice and streamline psychiatric drug development.
Addressing sample variability is crucial for robust biomarker extraction.