Details of a Researcher - KISHIMOTO Taishiro

熱意を理性でドライブせよ

千葉大学医学部附属病院次世代医療構想センター, 吉村健佑, 松本晴樹, 近藤克則 , 野口晴子 , 小谷和彦, 松本俊彦 , 石井洋介, 小笠原定久, 山口育子 , 鋪野紀好, 津野香奈美, 近藤尚己, 川上英良, 稲葉可奈子, 岸本泰士郎, 加藤浩晃, 佐々木淳 , ロギカ書房, 2025
Information technology and electronic health record to improve behavioral health services

Hilty D., Naslund J.A., Ahuja S., Torous J., Kishimoto T., Crawford A., Mental Health in a Digital World, 2021.01

　View Summary

We continue to increase our exchange of information through health technologies, used to access, disseminate, and analyze information. Clinical informatics is a rapidly expanding area and facilitates patient-centered care as defined by quality, affordability, and timely health care. This chapter covers developments in information systems, electronic health records, electronic communications with patients and staff (e.g., alerts, texts), behavioral health indicators and related digital advances to improve practice and research. The reader can learn how to set goals toward quality outcomes and be efficient while remaining patient-centered using technology, and adapt to technological components and processes used by systems. By grasping how systems are designed and tailored to collect data, clinicians can use technology to inform decisions and facilitate outcomes. Setting priorities involves input from all care participants, as well as technological competencies for the clinician and institutional/organizational. Patient, clinician, and institutional competencies for skills, attitudes, and behaviors can align clinical care, training, and research missions and stimulate quality improvement.
- Access to Document (DOI)
Autism Spectrum Disorderâ€™s Severity Prediction Model Using Utterance Features for Automatic Diagnosis Support

Sakishita M., Ogawa C., Tsuchiya K.J., Iwabuchi T., Kishimoto T., Kano Y., Studies in Computational Intelligence, 2020

　View Summary

Diagnoses of autism spectrum disorder (ASD) are difficult due to difference of interviewers and environments, etc. We show relations between utterance features and ASD severity scores, which were manually given by clinical psychologists. These scores are based on the Autism Diagnostic Observation Schedule (ADOS), which is the standard metrics for symptom evaluation for subjects who are suspected as ASD. We built our original corpus where we transcribed voice records of our ADOS evaluation experiment movies. Our corpus is the world largest as speech/dialog of ASD subjects, and there has been no such ADOS corpus available in Japanese language as far as we know. We investigated relationships between ADOS scores (severity) and our utterance features, automatically estimated their scores using support vector regression (SVR). Our average estimation errors were around error rates that human ADOS experts are required not to exceed. Because our detailed analysis for each part of the ADOS test (â€œpuzzle toy assembly + story tellingâ€ part and the â€œdepiction of a pictureâ€ part) shows different error rates, effectiveness of our features would depend on the contents of the records. Our entire results suggest a new automatic way to assist humansâ€™ diagnosis, which could help supporting language rehabilitation for individuals with ASD in future.
Large-Scale Dialog Corpus Towards Automatic Mental Disease Diagnosis

Sakishita M., Kishimoto T., Takinami A., Eguchi Y., Kano Y., Studies in Computational Intelligence, 2020

　View Summary

Recently, the number of people who are diagnosed as mental diseases is increasing. Efficient and objective diagnosis is important to start medical treatments in earlier stages. However, mental disease diagnosis is difficult to quantify criteria, because it is performed through conversations with patients, not by physical surveys. We aim to automate mental disease diagnosis in order to resolve these issues. We recorded conversations between psychologists and subjects to build our diagnosis speech corpus. Our subjects include healthy persons, people with mental diseases of depression, bipolar disorder, schizophrenia, anxiety and dementia. All of our subjects are diagnosed by doctors of psychiatry. Then we made accurate transcription manually, adding utterance time stamps, linguistic and non-linguistic annotations. Using our corpus, we performed feature analysis to find characteristics for each disease. We also tried automatic mental disease diagnosis by machine learning, while the number of sample data is few because we were still in our pilot study phase. We will increase the number of subjects in future.
エッセンシャル金融ジェロントロジー

駒村康平編, 岸本泰士郎,中村陽一, 江口洋子著, 慶應義塾大学出版会, 2019.10

display all >>

Page Top ▲

Papers 【 Display / hide 】

Collaborative Outcomes Study on Health and Functioning During Infection Times (COH-FIT): Global and Risk-Group Stratified Course of Well-Being and Mental Health During the COVID-19 Pandemic in Adolescents

Solmi M., Thompson T., Cortese S., Estradé A., Agorastos A., Radua J., Dragioti E., Vancampfort D., Thygesen L.C., Aschauer H., Schlögelhofer M., Aschauer E., Schneeberger A.A., Huber C.G., Hasler G., Conus P., Do Cuénod K.Q., von Känel R., Arrondo G., Fusar-Poli P., Gorwood P., Llorca P.M., Krebs M.O., Scanferla E., Kishimoto T., Rabbani G., Skonieczna-Żydecka K., Brambilla P., Favaro A., Takamiya A., Zoccante L., Colizzi M., Bourgin J., Kamiński K., Moghadasin M., Seedat S., Matthews E., Wells J., Vassilopoulou E., Gadelha A., Su K.P., Kwon J.S., Kim M., Lee T.Y., Papsuev O., Manková D., Boscutti A., Gerunda C., Saccon D., Righi E., Monaco F., Croatto G., Cereda G., Demurtas J., Brondino N., Veronese N., Enrico P., Politi P., Ciappolino V., Pfennig A., Bechdolf A., Meyer-Lindenberg A., Kahl K.G., Domschke K., Bauer M., Koutsouleris N., Winter S., Borgwardt S., Bitter I., Balazs J., Czobor P., Unoka Z., Mavridis D., Tsamakis K., Bozikas V.P., Tunvirachaisakul C., Maes M., Rungnirundorn T., Supasitthumrong T., Haque A., Brunoni A.R., Costardi C.G., Schuch F.B., Polanczyk G., Luiz J.M., Fonseca L., Aparicio L.V., Valvassori S.S., Nordentoft M., Vendsborg P., Hoffmann S.H., Sehli J., Sartorius N., Heuss S., Guinart D., Hamilton J., Kane J., Rubio J., Sand M., Koyanagi A.

Journal of the American Academy of Child and Adolescent Psychiatry 64 ( 4 ) 499 - 519 2025.04

ISSN 08908567

　View Summary

Objective: To identify the COVID-19 pandemic impact on well-being/mental health, coping strategies, and risk factors in adolescents worldwide. Method: This study was based on an anonymous online multi-national/multi-language survey in the general population (representative/weighted non-representative samples, 14-17 years of age), measuring change in well-being (World Health Organization–Five Well-Being Index [WHO-5]/range = 0-100) and psychopathology (validated composite P-score/range = 0-100), WHO-5 <50 and <29, pre- vs during COVID-19 pandemic (April 26, 2020-June 26, 2022). Coping strategies and 9 a priori– defined individual/cumulative risk factors were measured. A χ2, penalized cubic splines, linear regression, and correlation analyses were conducted. Results: Analyzing 8,115 of 8,762 initiated surveys (representative = 75.1%), the pre-pandemic WHO-5 and P-score remained stable during the study (excluding relevant recall bias/drift), but worsened during the pandemic by 5.55 ± 17.13 (SD) and 6.74 ± 16.06 points, respectively (effect size d = 0.27 and d = 0.28). The proportion of adolescents with WHO-5 scores suggesting depression screening (<50) and major depression (<29) increased from 9% to 17% and 2% to 6%. WHO-5 worsened (descending magnitude, with cumulative effect) in adolescents with a mental or physical disorder, female gender, and with school closure. Results were similar for the P-score, with the exception of school closure (not significant) and living in a low-income country, as well as not living in a large city (significant). Changes were significantly but minimally related to COVID-19 deaths/restrictions, returning to near–pre-pandemic values after >2 years. The 3 most subjectively effective coping strategies were Internet use, exercise/walking, and social contacts. Conclusion: Overall, well-being/mental health worsened (small effect sizes) during the early stages of the COVID-19 pandemic, especially in vulnerable subpopulations. Identified at-risk groups, association with pandemic-related measures, and coping strategies can inform individual behaviors and global public health strategies. Plain language summary: The impact of COVID-19 and related restrictions on the mental health of children and adolescents remains unclear. This study conducted an anonymous online survey with over 8,100 adolescents aged 14 to 17 years worldwide. Results showed a significant decline in wellbeing, with depression rates increasing from 9% to 17% returning to near- pre-pandemic values after about 2 years. Adolescents with preexisting mental health conditions, female youth, and those affected by school closures were particularly vulnerable. Effective coping strategies included internet use, exercise, and social contacts, highlighting areas for public health focus to support youth during pandemics. Study preregistration information: Physical and mental health impact of COVID-19 on children, adolescents, and their families: The Collaborative Outcomes study on Health and Functioning during Infection Times - Children and Adolescents (COH-FIT-C&A); https://doi.org/10.1016/j.jad.2021.09.090 Diversity & Inclusion Statement: We worked to ensure sex and gender balance in the recruitment of human participants. We worked to ensure race, ethnic, and/or other types of diversity in the recruitment of human participants. We worked to ensure that the study questionnaires were prepared in an inclusive way. The author list of this paper includes contributors from the location and/or community where the research was conducted who participated in the data collection, design, analysis, and/or interpretation of the work. We actively worked to promote inclusion of historically underrepresented racial and/or ethnic groups in science in our author group. We actively worked to promote sex and gender balance in our author group. One or more of the authors of this paper self-identifies as a member of one or more historically underrepresented racial and/or ethnic groups in science.
- Access to Document (DOI)
Electrodermal activity and skin temperature characteristics related to stress and depression: A 4-week observational study of office workers

Ishikawa Y., Sugio T., Shiga K., Izumi K., Minato K., Kitazawa M., Hanashiro S., Takemura R., Uchida H., Kishimoto T.

Journal of Affective Disorders Reports 20 2025.04

　View Summary

Background: Mental health problems, particularly depression, significantly impact the working-age population and incur substantial socioeconomic costs. Electrodermal activity (EDA) and skin temperature, measurable through wearable devices, have emerged as potential biomarkers of depression. However, there is a lack of long-term observational studies in real-world settings. Methods: This 4-week observational study analyzed data from 147 office workers across 11 Japanese companies. Participants wore wearable devices during working hours for four weeks to collect EDA and skin temperature data. Depressive symptoms were assessed using the Patient Health Questionnaire-9 (PHQ-9), and stress was assessed using the 10-item version of the Perceived Stress Scale (PSS-10). The associations between electrodermal activity, skin temperature, and depressive/stress states were analyzed using the Mann–Whitney U test and logistic regression analysis. Results: Participants with PHQ-9 scores ≥5 showed higher median skin conductance and median tonic signal, although these differences lost statistical significance after Bonferroni correction. Logistic regression analysis revealed a negative correlation between age and both PHQ-9 and PSS-10 scores (OR=0.525, 0.646). The mean and standard deviation of skin temperature showed a positive association with depressive symptoms (OR=1.687, 1.733), while normalized EDASymp showed a negative association (OR=0.564). Conclusions: This study suggests that EDA and skin temperature, measured by wearable devices, may be associated with depressive symptoms among office workers. These physiological indicators could be utilized for mental health monitoring and early intervention in the workplace. Further validation with larger samples, and long-term follow-up studies, are warranted.
- Access to Document (DOI)
特集精神疾患診療へのデジタルツールの活用精神疾患患者に対する非薬物療法におけるデジタルツールの開発の流れと展望

古川渉太, 木下翔太郎, 岸本泰士郎

精神医学 (株式会社医学書院) 67 ( 1 ) 5 - 11 2025.01

ISSN 04881281
- Access to Document (DOI)
- CiNii Research
Collaborative outcomes study on health and functioning during infection times (COH-FIT): Insights on modifiable and non-modifiable risk and protective factors for wellbeing and mental health during the COVID-19 pandemic from multivariable and network analyses

Solmi M., Thompson T., Cortese S., Estradé A., Agorastos A., Radua J., Dragioti E., Vancampfort D., Thygesen L.C., Aschauer H., Schlögelhofer M., Aschauer E., Schneeberger A., Huber C.G., Hasler G., Conus P., Cuénod K.Q.D., von Känel R., Arrondo G., Fusar-Poli P., Gorwood P., Llorca P.M., Krebs M.O., Scanferla E., Kishimoto T., Rabbani G., Skonieczna-Żydecka K., Brambilla P., Favaro A., Takamiya A., Zoccante L., Colizzi M., Bourgin J., Kamiński K., Moghadasin M., Seedat S., Matthews E., Wells J., Vassilopoulou E., Gadelha A., Su K.P., Kwon J.S., Kim M., Lee T.Y., Papsuev O., Manková D., Boscutti A., Gerunda C., Saccon D., Righi E., Monaco F., Croatto G., Cereda G., Demurtas J., Brondino N., Veronese N., Enrico P., Politi P., Ciappolino V., Pfennig A., Bechdolf A., Meyer-Lindenberg A., Kahl K.G., Domschke K., Bauer M., Koutsouleris N., Winter S., Borgwardt S., Bitter I., Balazs J., Czobor P., Unoka Z., Mavridis D., Tsamakis K., Bozikas V.P., Tunvirachaisakul C., Maes M., Rungnirundorn T., Supasitthumrong T., Haque A., Brunoni A.R., Costardi C.G., Schuch F.B., Polanczyk G., Luiz J.M., Fonseca L., Aparicio L.V., Valvassori S.S., Nordentoft M., Vendsborg P., Hoffmann S.H., Sehli J., Sartorius N., Heuss S., Guinart D., Hamilton J., Kane J., Rubio J., Sand M., Koyanagi A.

European Neuropsychopharmacology 90 1 - 15 2025.01

ISSN 0924977X

　View Summary

There is no multi-country/multi-language study testing a-priori multivariable associations between non-modifiable/modifiable factors and validated wellbeing/multidimensional mental health outcomes before/during the COVID-19 pandemic. Moreover, studies during COVID-19 pandemic generally do not report on representative/weighted non-probability samples. The Collaborative Outcomes study on Health and Functioning during Infection Times (COH-FIT) is a multi-country/multi-language survey conducting multivariable/LASSO-regularized regression models and network analyses to identify modifiable/non-modifiable factors associated with wellbeing (WHO-5)/composite psychopathology (P-score) change. It enrolled general population-representative/weighted-non-probability samples (26/04/2020-19/06/2022). Participants included 121,066 adults (age=42±15.9 years, females=64 %, representative sample=29 %) WHO-5/P-score worsened (SMD=0.53/SMD=0.74), especially initially during the pandemic. We identified 15 modifiable/nine non-modifiable risk and 13 modifiable/three non-modifiable protective factors for WHO-5, 16 modifiable/11 non-modifiable risk and 10 modifiable/six non-modifiable protective factors for P-score. The 12 shared risk/protective factors with highest centrality (network-analysis) were, for non-modifiable factors, country income, ethnicity, age, gender, education, mental disorder history, COVID-19-related restrictions, urbanicity, physical disorder history, household room numbers and green space, and socioeconomic status. For modifiable factors, we identified medications, learning, internet, pet-ownership, working and religion as coping strategies, plus pre-pandemic levels of stress, fear, TV, social media or reading time, and COVID-19 information. In multivariable models, for WHO-5, additional non-modifiable factors with |B|>1 were income loss, COVID-19 deaths. For modifiable factors we identified pre-pandemic levels of social functioning, hobbies, frustration and loneliness, and social interactions as coping strategy. For P-scores, additional non-modifiable/modifiable factors were income loss, pre-pandemic infection fear, and social interactions as coping strategy. COH-FIT identified vulnerable sub-populations and actionable individual/environmental factors to protect well-being/mental health during crisis times. Results inform public health policies, and clinical practice.
- Access to Document (DOI)
Dementia and MCI Detection Based on Comprehensive Facial Expression Analysis From Videos During Conversation

Okunishi T., Zheng C., Bouazizi M., Ohtsuki T., Kitazawa M., Horigome T., Kishimoto T.

IEEE Journal of Biomedical and Health Informatics 2025

ISSN 21682194

　View Summary

The development of a cost-effective digital biomarker for detecting dementia is highly needed. While numerous studies have explored dementia detection through speech and natural language analysis, only a few studies have focused on dementia detection using face video recordings, and more in-depth research is needed. In this paper, we propose a method for detecting dementia and mild cognitive impairment (MCI), a pre-dementia stage, by utilizing four types of facial expression features extracted from recorded videos of participants. These features include Action Units, emotion categories, Valence-Arousal, and face embeddings. From the above features obtained from each video frame, various statistical information was extracted and used as features, and predictions were performed using a decision tree-based model. Our method was evaluated using face video recordings during conversations. The method achieved an area under the receiver operating characteristic curve (AUC) of 0.933 for dementia detection and 0.889 for MCI detection. Statistical analysis of facial expression features revealed that participants with dementia had fewer positive emotions, more negative emotions, and lower valence and arousal than healthy participants. These results indicate that the proposed method could serve as an explainable screening tool for the early detection of dementia and MCI.
- Access to Document (DOI)

display all >>

Page Top ▲

Papers, etc., Registered in KOARA 【 Display / hide 】

A cross sectional study of fecal microbiome among Japanese children with autistic spectrum disorder, attention deficit hyperactivity disorder, their siblings and healthy volunteers.

Kishimoto, Taishiro

学事振興資金研究成果実績報告書 (慶應義塾大学) 2020
Research towards implementation of medical AI based on big data

Kishimoto, Taishirō

科学研究費補助金研究成果報告書 2020
A cross sectional study of fecal microbiome among Japanese children with autistic spectrum disorder, attention deficit hyperactivity disorder, their siblings and healthy volunteers.

Kishimoto, Taishiro

学事振興資金研究成果実績報告書 (慶應義塾大学) 2019
A cross sectional study of fecal microbiome among Japanese children with autistic spectrum disorder, attention deficit hyperactivity disorder, their siblings and healthy volunteers.

Kishimoto, Taishiro

学事振興資金研究成果実績報告書 (慶應義塾大学) 2018
Development of a technology to quantify psychiatric symptoms utilizing behavioral and physiological data

Kishimoto, Taishiro

科学研究費補助金研究成果報告書 2017

Page Top ▲

Reviews, Commentaries, etc. 【 Display / hide 】

【精神薬理学における自律神経機能の役割】ウェアラブルデバイスを用いた自律神経機能と活動度の測定に基づくメンタルヘルス評価

古関恵太, 堀江遼顕, 石川祐希, 木下翔太郎, 岸本泰士郎

臨床精神薬理 ((株)星和書店) 28 ( 2 ) 189 - 196 2025.02

ISSN 1343-3474

　View Summary

ウェアラブルデバイスを精神疾患の診断・予測に利用する研究が近年数多く行われている。ウェアラブルデバイスのセンサーによって測定された心拍変動や皮膚電気活動,加速度を基にした自律神経機能および活動度の評価はうつ病,双極症,不安症,神経発達症など多様な疾患において有効性が示されている。ウェアラブルデバイスからのデータと人工知能(artificial intelligence:AI)技術を組み合わせることで従来の医療面接や自己報告に依存した診断方法を補完し,精神疾患の早期発見や遠隔モニタリングが可能となり,精密治療に繋がることが期待される。今後の研究と技術の進展によりウェアラブルデバイスはメンタルヘルスケアの新たなスタンダードとなる可能性がある。(著者抄録)
Japan's Telepsychiatry Dissemination: Current Status and Challenges

Kinoshita S., Kishimoto T.

Journal of Medical Internet Research 27 e22849 2025

ISSN 1438-8871
- Access to Document (DOI)
Ageing population in Japan: immediate shake-up in healthcare required

Kinoshita S., Kishimoto T.

QJM: An International Journal of Medicine 117 ( 12 ) 829 - 830 2024.12

ISSN 14602725
- Access to Document (DOI)
【精神科病院における医療DX】精神科病院における医療DX　オンライン診療

木下翔太郎, 岸本泰士郎

日本精神科病院協会雑誌 ((公社)日本精神科病院協会) 43 ( 12 ) 28 - 34 2024.12

ISSN 1347-4103
保険収載された遠隔医療分野の現状と将来　精神科領域におけるオンライン診療のエビデンス構築の経過と利用促進に向けての考察

岸本泰士郎

医療情報学連合大会論文集 ((一社)日本医療情報学会) 44回 258 - 260 2024.11

ISSN 1347-8508

display all >>

Page Top ▲

Presentations 【 Display / hide 】

AI時代の精神科医療の展望

岸本泰士郎

第1回日本外来精神医学会学術総会,

2024.09
,
Oral presentation (invited, special)
精神科領域におけるICTやAI活用の試み.

岸本泰士郎

医用画像情報学会　第185回大会,

2024.09
,
Oral presentation (invited, special)
情報通信技術やAIを活用した精神科医療の展望

岸本泰士郎

第27回多文化間精神医学会学術総会,

2024.09
,
Oral presentation (invited, special)
Quantifying the Invisible: Developing a software as a medical device (SaMD) for psychiatric disorders.

TAISHIRO KISHIMOTO

0th International Conference in Vietnam on the Developing of Biomedical Engineering, (KOBE) ,

2024.07
,
Oral presentation (invited, special)
AI時代の精神科医療の展望

岸本泰士郎

第21回日本うつ病学会総,

2024.07
,
Oral presentation (invited, special)

display all >>

Page Top ▲

Research Projects of Competitive Funds, etc. 【 Display / hide 】

障害者政策総合研究事業「地域医療に根差して行われるオンライン診療のための医師向け手引書の策定」

2024

-

Present

厚生労働科学研究費, Principal investigator
令和5年度予防・健康づくりの社会実装に向けた研究開発基盤整備事業（エビデンス構築促進事業）「将来の認知機能予測に基づくテーラーメイド行動変容プログラム開発」

2023

-

Present

日本医療研究開発機構（AMED）, Commissioned ｒesearch, Coinvestigator(s)
戦略的イノベーション創造プログラム包摂的コミュニティプラットフォームの構築「AIホスピタルによる高度診断・治療システム」SIP事業「高齢者が生涯にわたって自立的に経済活動ができる包摂的な社会経済システム構築」

2023

-

Present

医薬基盤・健康・栄養研究所（NIBIOHN）, Coinvestigator(s)
研究開発とSociety5.0との橋渡しプログラム（BRIDGE）SIP事業「未来型医療システムの基盤となるAIホスピタルモデルの構築」

2023

医薬基盤・健康・栄養研究所（NIBIOHN）, Coinvestigator(s)
SNS・新聞記事・議会議事録を用いたAIによる世論形成過程と政治家の応答性の分析

2022.04

-

2027.03

Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (B), Coinvestigator(s)

　View Summary

世論が政治家の言動や政治的出来事に対して合理的に反応しているかを検証することは、現代民主主義においてアカウンタビリティが確保されているかをチェックする上で重要な課題である。本研究では、SNSデータ（引用された新聞記事を含む）と国会議事録（文字・映像）を対象に、機械学習による精細な話し言葉解析器と心理状態分析器・嘘検出器を用いて、これら情報の受信・発信者の状態とその変化をミクロで推測する。さらにマクロな集団としての我が国の世論形成過程、政府・政治家と世論の関係を時系列モデルで分析し、可視化する。具体的なテーマとしては対外国人意識を重点的に扱う。
世論が政治家の言動や政治的出来事に対して合理的に反応しているかを検証することは、現代民主主義においてアカウンタビリティが確保されているかをチェックする上で重要な課題である。本研究では、SNSデータ（引用された新聞記事を含む）と国会議事録（文字・映像）を対象に、機械学習による精細な話し言葉解析器と心理状態分析器・嘘検出器を用いて、これら情報の受信・発信者の状態とその変化をミクロで推測する。さらにマクロな集団としての我が国の世論形成過程、政府・政治家と世論の関係を時系列モデルで分析し、可視化する。具体的なテーマとしては対外国人意識を重点的に扱う。
初年度にあたる本年度は、クラウドソーシングによる大規模ウェブ調査、嘘検出器作成のためのコーパス設計、SNS投稿の影響推測、含意関係認識に取り組んだ。クラウドソーシングによる大規模ウェブ調査では、これまでになかったユーザの属性を表す多面的な質問と、政治的な意識を紐づけられるよう設計した。SNS投稿の影響推測では、テキスト自体がどの程度その影響度を決定するかを定量的に推測できた。
初年度にあたる本年度は、クラウドソーシングによる大規模ウェブ調査、嘘検出器作成のためのコーパス設計、SNS投稿の影響推測、含意関係認識に取り組んだ。クラウドソーシングによる大規模ウェブ調査では、これまでになかったユーザの属性を表す多面的な質問と、政治的な意識を紐づけられるよう設計した。SNS投稿の影響推測では、テキスト自体がどの程度その影響度を決定するかを定量的に推測できた。
aクラウドソーシングによる大規模ウェブ調査を続行しその規模を増大させる。また、調査結果の分析と利用を進める。
LLM（大規模言語モデル）の性能向上をうけ、どこまで利用可能か、性能比較とシステムへの組み込みを検討する。

display all >>

Page Top ▲

Awards 【 Display / hide 】

2019年度慶應医学賞ライジング・スター賞

2020.01, 慶應義塾大学
Keio Techno Mall Lion Award（研究室として受賞）

2017.12
国際学会発表賞

2014.06, 第110回日本精神神経科学会学術総会
国際学会発表賞

2014.06, 日本精神神経科学会
Japanese Society of Neuropsychopharmacology Excellent Presentation Award for CINP 2014

2014.06, CINP 2014

display all >>

Page Top ▲

Courses Taught 【 Display / hide 】

LECTURE SERIES, PSYCHIATRY

2025
BEHAVIORAL SCIENCE: SEMINAR

2025
BEHAVIORAL SCIENCE: PRACTICE

2025
BEHAVIORAL SCIENCE

2025
ADVANCED BEHAVIORAL SCIENCE

2025

display all >>

Page Top ▲

Academic Activities 【 Display / hide 】

精神疾患の克服と障害支援にむけた研究推進の提言（日本精神神経学会、日本生物学的精神医学会、日本神経精神薬理学会、日本うつ病学会、日本統合失調症学会、他7学会、日本脳科学関連連合）策定委員

2022.07

-

Present
国立精神・神経医療研究センター臨床研究審査委員会技術専門員

2022.04

-

2024.03
Schizophrenia Bulletin 編集委員

2021

-

Present
JMA Journal 編集委員

2021

-

Present
お酒の健康科学財団競争的資金審査員

2020

-

Present

display all >>

Page Top ▲

Memberships in Academic Societies 【 Display / hide 】

日本産業衛生学会,

2024

-

Present
人工知能学会会員,

2022

-

Present
日本メディカルAI学会,

2018

-

Present
日本うつ病学会,

2016

-

Present
日本総合病院精神医学会,

2015

-

Present

display all >>

Page Top ▲

Committee Experiences 【 Display / hide 】

2023

-

Present

脳科連産学連携諮問委員会WG4 , 日本脳科学関連学会連合
2023

-

Present

医療DXに関する委員会委員, 日本精神神経学会
2022

-

Present

ランスレーショナル・メディカル・サイエンス委員, 日本神経精神薬理学会
2022

-

Present

運営会議メンバー（評議員）, 日本遠隔医療学会
2022

-

Present

監事, 日本神経精神薬理学会

display all >>

Page Top ▲

KEIO RESEARCHERS INFORMATION SYSTEM

Profile Summary 【 Display / hide 】

Profile Summary 【 Display / hide 】

Career 【 Display / hide 】

Career 【 Display / hide 】

Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

Licenses and Qualifications 【 Display / hide 】

Licenses and Qualifications 【 Display / hide 】

Books 【 Display / hide 】

Books 【 Display / hide 】

Papers 【 Display / hide 】

Papers 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

Papers, etc., Registered in KOARA 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

Presentations 【 Display / hide 】

Presentations 【 Display / hide 】

Research Projects of Competitive Funds, etc. 【 Display / hide 】

Research Projects of Competitive Funds, etc. 【 Display / hide 】

Awards 【 Display / hide 】

Awards 【 Display / hide 】

Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

Academic Activities 【 Display / hide 】

Academic Activities 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

Memberships in Academic Societies 【 Display / hide 】

Committee Experiences 【 Display / hide 】

Committee Experiences 【 Display / hide 】