The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology
| العنوان: | The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology |
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| المؤلفون: | Junichi Murakami, Kei Funaki, Yasue Mitsukura, Koichi Shinoda, Kuo-ching Liang, Shogyoku Bun, Tifani Warnita, Brian Sumali, Akihiro Takamiya, Aiko Kishi, Mizuki Yotsui, Taishiro Kishimoto, Toshiaki Kikuchi, Masayuki Tomita, Yasubumi Sakakibara, Yuki Tazawa, Masaru Mimura, Yoko Eguchi, Momoko Kitazawa, Fujita Takanori, Michitaka Yoshimura, Toyoshiba Hiroyoshi, Toshiro Horigome |
| المصدر: | Contemporary Clinical Trials Communications, Vol 19, Iss, Pp 100649-(2020) Contemporary Clinical Trials Communications |
| بيانات النشر: | ELSEVIER INC, 2020. |
| سنة النشر: | 2020 |
| مصطلحات موضوعية: | PREDICTION, AMED, Japan Agency for Medical Research and Development, Wearable computer, SVM, Support Vector Machine, Disease, Research & Experimental Medicine, MMSE, Mini-Mental State Examination, UI, uncertainty interval, MARS, Motor Agitation and Retardation Scale, Motion (physics), 0302 clinical medicine, MDD, Major depressive disorder, Medicine, Neurocognitive disorder, 030212 general & internal medicine, Cognitive decline, Depression (differential diagnoses), SCALE, SVR, Support Vector Regression, lcsh:R5-920, medicine.diagnostic_test, Depression, ABNORMALITIES, F0, fundamental frequency, BNN, Bayesian Neural Networks, LM, Wechsler Memory Scale-Revised Logical Memory, GDS, Geriatric Depression Scale, IEC, International Electrotechnical Commission, General Medicine, CPP, cepstral peak prominence, F1, F2, F3, first, second, and third formant frequencies, STATE, SCID, Structural Clinical Interview for DSM-5, MADRS, Montgomery-Asberg Depression Rating Scale, Medicine, Research & Experimental, MCI, mild cognitive impairment, CNN, Convolutional Neural Networks, DSM-5, Diagnostic and Statistical Manual of Mental Disorders, Fifth Edition, RF, Random Forest, Screening, Major depressive disorder, UMIN, University Hospital Medical Information Network, lcsh:Medicine (General), Life Sciences & Biomedicine, M.I.N.I., Mini-International Neuropsychiatric Interview, MoCA, Montreal Cognitive Assessment, medicine.medical_specialty, PROMPT, Project for Objective Measures Using Computational Psychiatry Technology, RGB, red, green, blue, PSQI, Pittsburgh Sleep Quality Index, MFCC, mel-frequency cepstrum coefficients, BIOMARKERS, HAM-D, Hamilton Depression Rating Scale, FedRAMP, Federal Risk and Authorization Management Program, MELANCHOLIA, Article, PET, positron emission tomography, 03 medical and health sciences, Adabag, Adaptive Bagging, YMRS, Young Mania Rating Scale, Machine learning, Bipolar disorder, Psychiatry, Pharmacology, Facial expression, Mini–Mental State Examination, Science & Technology, business.industry, NPI, Neuropsychiatric Inventory, Public health, Natural language processing, BDI-II, Beck Depression Inventory, Second Edition, MAJOR DEPRESSION, medicine.disease, UV, ultraviolet, BD, Bipolar disorder, YLDs, years lived with disability, Clinical trial, SEVERITY, LSTM, Long Short-Term Memory Networks, CDR, Clinical Dementia Rating, GCNN, Gated Convolutional Neural Networks, ISO, International Organization for Standardization, MEASUREMENT ERROR, business, MRI, magnetic resonance imaging, Neurocognitive, RETARDATION, Adaboost, Adaptive Boosting, CDT, Clock Drawing Test, 030217 neurology & neurosurgery |
| الوصف: | BackgroundDepressive 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.MethodsThis 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 follow-up. 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.DiscussionThe 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 RegistrationUMIN000021396, University Hospital Medical Information Network (UMIN) |
| وصف الملف: | Electronic-eCollection |
| اللغة: | English |
| URL الوصول: | https://explore.openaire.eu/search/publication?articleId=doi_dedup___::1e61fa1932416286b0f9e971927ed6b4 https://lirias.kuleuven.be/handle/20.500.12942/715730 |
| Rights: | OPEN |
| رقم الانضمام: | edsair.doi.dedup.....1e61fa1932416286b0f9e971927ed6b4 |
| قاعدة البيانات: | OpenAIRE |
| الوصف غير متاح. |