The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology.
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
Journal
Contemporary clinical trials communications
ISSN: 2451-8654
Titre abrégé: Contemp Clin Trials Commun
Pays: Netherlands
ID NLM: 101671157
Informations de publication
Date de publication:
Sep 2020
Sep 2020
Historique:
received:
27
04
2020
revised:
06
08
2020
accepted:
16
08
2020
entrez:
11
9
2020
pubmed:
12
9
2020
medline:
12
9
2020
Statut:
epublish
Résumé
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. 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. 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. UMIN000021396, University Hospital Medical Information Network (UMIN).
Identifiants
pubmed: 32913919
doi: 10.1016/j.conctc.2020.100649
pii: S2451-8654(20)30133-2
pii: 100649
pmc: PMC7473877
doi:
Types de publication
Journal Article
Langues
eng
Pagination
100649Informations de copyright
© 2020 The Authors.
Déclaration de conflit d'intérêts
T. Kishimoto has received consultant fees from Otsuka, Pfizer, and Dainippon Sumitomo, and speaker's honoraria from Banyu, Eli Lilly, Dainippon Sumitomo, Janssen, Novartis, Otsuka, and Pfizer. KF has received speaker's honoraria from Novartis and Otsuka. HT is an employee of FRONTEO. TH received speaker's honoraria from Yoishitomi. T. Kikuchi has received speaker's honoraria from Astellas, Dainippon Sumitomo, Eli Lilly, Janssen, MSD, Otsuka, Yoshitomi Yakuhin, Pfizer, and Takeda. JM has received speaker's honoraria from Eli Lilly, Janssen, Otsuka, MSD, Shionogi, and Pfizer. MM has received speaker's honoraria from Daiichi Sankyo, Dainippon-Sumitomo Pharma, Eisai, Eli Lilly, Fuji Film RI Pharma, Janssen Pharmaceutical, Mochida Pharmaceutical, MSD, Nippon Chemipher, Novartis Pharma, Ono Yakuhin, Otsuka Pharmaceutical, Pfizer, Takeda Yakuhin, Tsumura, and Yoshitomi Yakuhin. Also, he received grants from Daiichi Sankyo, Eisai, Pfizer, Shionogi, Takeda, Tanabe Mitsubishi, and Tsumura. Other authors have no conflict of interest.
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