Unique ID issued by UMIN | UMIN000029874 |
---|---|
Receipt number | R000033918 |
Scientific Title | Natural Language Processing on SNS Data to Extract Features of Psychiatric Disorders: A Pilot Study |
Date of disclosure of the study information | 2017/11/08 |
Last modified on | 2024/05/14 14:09:01 |
Natural Language Processing on SNS Data to Extract Features of Psychiatric Disorders: A Pilot Study
Natural Language Processing on SNS Data to Extract Features of Psychiatric Disorders: A Pilot Study
Natural Language Processing on SNS Data to Extract Features of Psychiatric Disorders: A Pilot Study
Natural Language Processing on SNS Data to Extract Features of Psychiatric Disorders: A Pilot Study
Japan |
1. Patients with major depressive disorder, bipolar I/II disorder, schizophrenia, anxiety disorders, and major/mild neurocognitive disorder by DSM-5
2. Healthy volunteers
Psychiatry | Adult |
Others
NO
To develop an algorithm to identify features of major depressive disorder, bipolar I/II disorder, schizophrenia, anxiety disorders, and major/mild neurocognitive disorder utilizing natural language processing based on SNS text.
Safety
To see if the data collection according to the protocol is feasible and to identify the problems to conduct further study.
Observational
20 | years-old | <= |
Not applicable |
Male and Female
As patients
(1)
Out/in-patients at the study site diagnosed as Major Depressive Disorder, Bipolar I/II Disorder, Schizophrenia, Anxiety Disorders, Major/Mild Neurocognitive Disorder, according to DSM-5.
(2) 20 years old or older.
(3) Decisionally not impaired judged by treating physician. If judged as decisionally impaired, patients' guardians should give consent.
As healthy volunteers
(1) Healthy volunteers who offered to participate to the study through web site.
(2) 20 years old or older.
As patients
(1) Patients whose illness can exacerbate by interview of the study.
(2) Patients who have comorbidities that can interfere with posting to social network service; such as patients with hand paralysis or visual impairment.
(3) Those who are considered to be ineligible by the PI or investigators.
As healthy volunteers
(1) Those who have comorbidities that can interfere with posting to social network service; such as patients with hand paralysis or visual impairment.
(2) Those who are considered to be ineligible by the PI or investigators.
30
1st name | Taishiro |
Middle name | |
Last name | Kishimoto |
Keio University School of Medicine
Hills Joint Research Laboratory for Future Preventive Medicine and Wellnes
106-0032
Roppongi Hills North Tower 7F, 6-2-31 Roppongi, minato-ku, Tokyo, Japan
03-5786-0006
tkishimoto@keio.jp
1st name | Momoko |
Middle name | |
Last name | Kitazawa |
Keio University School of Medicine
Department of Neuropsychiatry
160-8582
35 Shinanomachi, Shinjuku-ku, Tokyo, JAPAN
03-5363-3492
m-kitazawa@keio.jp
Keio University School of Medicine
Japan Science and Technology Agency (JST)
Japanese Governmental office
Japan
Shizuoka University
The Clinical and Translational Research Center
35 Shinanomachi, Shinjuku-ku, Tokyo, JAPAN
03-3353-1211
med-rinri-jimu@adst.keio.ac.jp
NO
慶應義塾大学病院(東京都)
2017 | Year | 11 | Month | 08 | Day |
Unpublished
30
No longer recruiting
2017 | Year | 10 | Month | 06 | Day |
2018 | Year | 06 | Month | 25 | Day |
2017 | Year | 11 | Month | 08 | Day |
2027 | Year | 05 | Month | 31 | Day |
Patients (n=5 for each diagnosis) who are diagnosed as 1) major depressive disorders or bipolar I/II disorder, 2) schizophrenia, 3) anxiety disorders, 4) major/mild neurocognitive disorder according to DSM-5 and healthy volunteers (n=10) are collected their input texts on social network service.
In addition, patients are assessed their symptom severity by rating scales respectively shown below.
1) Major depressive disorders or bipolar I/II disorder
Hamilton rating scale for depression
Young mania rating scale
2) Schizophrenia
Brief psychiatric rating scale
3) Anxiety Disorders
The state-trait anxiety inventory
4) Major/mild neurocognitive disorder
Clinical dementia rating
Mini-mental scale examination
Data collected are analyzed utilizing natural language processing and the features that are related to each diagnosis are identified through machine learning approach.
Through this procedure, sample size calculation will be conducted.
2017 | Year | 11 | Month | 08 | Day |
2024 | Year | 05 | Month | 14 | Day |
Value
https://center6.umin.ac.jp/cgi-open-bin/ctr_e/ctr_view.cgi?recptno=R000033918