KEIO RESEARCHERS INFORMATION SYSTEM

Profile Summary 【 Display / hide 】

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Career 【 Display / hide 】

Career 【 Display / hide 】

• 2000.05

  -

  2001.03

  慶應義塾大学医学部, 精神神経科学教室, 研修医

• 2001.04

  -

  2003.06

  国家公務員共済組合連合会　立川病院, 神経科

• 2003.07

  -

  2004.03

  医療法人財団厚生協会　大泉病院

• 2004.04

  -

  2009.11

  医療法人財団厚生協会　大泉病院, 副医長

• 2009.04

  -

  2009.11

  医療法人財団厚生協会　大泉病院, 医長

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Academic Background 【 Display / hide 】

Academic Background 【 Display / hide 】

• 1992.04

  The University of Tokyo, 理科 II 類

  University, Other

• 1994.04

  -

  2000.03

  慶應義塾大学, 医学部

  University, Graduated

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

• 博士（医学）, 慶應義塾大学, Coursework, 2009.02

Licenses and Qualifications 【 Display / hide 】

Licenses and Qualifications 【 Display / hide 】

• 医師免許, 2000.05

• 精神保健指定医, 2005.12

• 日本精神神経科学会　専門医, 2008.04

• 日本英語検定　１級, 2014.07

• 臨床精神神経薬理学　専門医, 2016.11

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Books 【 Display / hide 】

Books 【 Display / hide 】

• 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 Disorder’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 (“puzzle toy assembly + story telling” part and the “depiction of a picture” 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 humans’ 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

• 本田明編:精神科身体合併症マニュアル第２版

  桑原達郎, 野村総一郎,岸本 泰士郎ほか, 医学書院, 2018.06

  Scope: 307-313,331-334

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Papers 【 Display / hide 】

Papers 【 Display / hide 】

• 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)

• Global and risk-group stratified well-being and mental health during the COVID-19 pandemic in adults: Results from the international COH-FIT Study

  Solmi M., Thompson T., Estradé A., Agorastos A., Radua J., Cortese S., 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.

  Psychiatry Research 342   115972 2024.12

  ISSN  01651781

  　View Summary

  International studies measuring wellbeing/multidimensional mental health before/ during the COVID-19 pandemic, including representative samples for >2 years, identifying risk groups and coping strategies are lacking. COH-FIT is an online, international, anonymous survey measuring changes in well-being (WHO-5) and a composite psychopathology P-score, and their associations with COVID-19 deaths/restrictions, 12 a-priori defined risk individual/cumulative factors, and coping strategies during COVID-19 pandemic (26/04/2020-26/06/2022) in 30 languages (representative, weighted non-representative, adults). T-test, χ2, penalized cubic splines, linear regression, correlation analyses were conducted. Analyzing 121,066/142,364 initiated surveys, WHO-5/P-score worsened intra-pandemic by 11.1±21.1/13.2±17.9 points (effect size d=0.50/0.60) (comparable results in representative/weighted non-probability samples). Persons with WHO-5 scores indicative of depression screening (<50, 13% to 32%) and major depression (<29, 3% to 12%) significantly increased. WHO-5 worsened from those with mental disorders, female sex, COVID-19-related loss, low-income country location, physical disorders, healthcare worker occupations, large city location, COVID-19 infection, unemployment, first-generation immigration, to age=18-29 with a cumulative effect. Similar findings emerged for P-score. Changes were significantly but minimally related to COVID-19 deaths, returning to near-pre-pandemic values after >2 years. The most subjectively effective coping strategies were exercise and walking, internet use, social contacts. Identified risk groups, coping strategies and outcome trajectories can inform global public health strategies.

  • Access to Document (DOI)

• Amyloid-β prediction machine learning model using source-based morphometry across neurocognitive disorders

  Momota Y., Bun S., Hirano J., Kamiya K., Ueda R., Iwabuchi Y., Takahata K., Yamamoto Y., Tezuka T., Kubota M., Seki M., Shikimoto R., Mimura Y., Kishimoto T., Tabuchi H., Jinzaki M., Ito D., Mimura M.

  Scientific Reports 14 ( 1 ) 7633 2024.12

  ISSN  2045-2322

  　View Summary

  Previous studies have developed and explored magnetic resonance imaging (MRI)-based machine learning models for predicting Alzheimer’s disease (AD). However, limited research has focused on models incorporating diverse patient populations. This study aimed to build a clinically useful prediction model for amyloid-beta (Aβ) deposition using source-based morphometry, using a data-driven algorithm based on independent component analyses. Additionally, we assessed how the predictive accuracies varied with the feature combinations. Data from 118 participants clinically diagnosed with various conditions such as AD, mild cognitive impairment, frontotemporal lobar degeneration, corticobasal syndrome, progressive supranuclear palsy, and psychiatric disorders, as well as healthy controls were used for the development of the model. We used structural MR images, cognitive test results, and apolipoprotein E status for feature selection. Three-dimensional T1-weighted images were preprocessed into voxel-based gray matter images and then subjected to source-based morphometry. We used a support vector machine as a classifier. We applied SHapley Additive exPlanations, a game-theoretical approach, to ensure model accountability. The final model that was based on MR-images, cognitive test results, and apolipoprotein E status yielded 89.8% accuracy and a receiver operating characteristic curve of 0.888. The model based on MR-images alone showed 84.7% accuracy. Aβ-positivity was correctly detected in non-AD patients. One of the seven independent components derived from source-based morphometry was considered to represent an AD-related gray matter volume pattern and showed the strongest impact on the model output. Aβ-positivity across neurological and psychiatric disorders was predicted with moderate-to-high accuracy and was associated with a probable AD-related gray matter volume pattern. An MRI-based data-driven machine learning approach can be beneficial as a diagnostic aid.

  • Access to Document (DOI)

• Updating the Japanese Healthcare System to Meet the Needs of an Aging Society

  Kinoshita, S; Kishimoto, T

  JMA JOURNAL  2024.10

  ISSN  2433-328X

  • Access to Document (DOI)

• A comparative study on dietary diversity and gut microbial diversity in children with autism spectrum disorder, attention-deficit hyperactivity disorder, their neurotypical siblings, and non-related neurotypical volunteers: a cross-sectional study

  Kurokawa S., Nomura K., Sanada K., Miyaho K., Ishii C., Fukuda S., Iwamoto C., Naraoka M., Yoneda S., Imafuku M., Matsuzaki J., Saito Y., Mimura M., Kishimoto T.

  Journal of Child Psychology and Psychiatry and Allied Disciplines 65 ( 9 ) 1184 - 1195 2024.09

  ISSN  00219630

  　View Summary

  Background: Previous research has shown a significant link between gut microbiota in children with autism spectrum disorder (ASD) and attention-deficit hyperactivity disorder (ADHD). However, much remains unknown because of the heterogeneity of disorders and the potential confounders such as dietary patterns and control group variations. Methods: Children aged 6–12 years who had been clinically diagnosed with ASD and/or ADHD, their unaffected neurotypical siblings, and non-related neurotypical volunteers were recruited cross-sectionally. The ASD diagnosis was confirmed using the Autism Diagnostic Observation Schedule-2 (ADOS-2) in all patients, including those with ADHD. Standardized DNA extraction and sequencing methods were used to compare gut microbial alpha-diversity among the groups. Dietary diversity was calculated from a standardized dietary questionnaire form. We compared the difference in gut microbiome between patients with ASD and/or ADHD with neurotypical siblings and non-related neurotypical controls. Results: Ninety-eight subjects were included in the study (18 with ASD, 19 with ADHD, 20 with both ASD and ADHD, 13 neurotypical siblings, and 28 non-related neurotypical controls). The alpha-diversity indices, such as Chao 1 and Shannon index, showed a significant difference between the groups in a Linear mixed-effect model (F(4, 93) = 4.539, p =.02), (F(4, 93) = 3.185, p =.017), respectively. In a post-hoc pairwise comparison, patients with ASD had lower alpha-diversity compared with non-related controls after Bonferroni correction. Dietary diversity shown in Shannon index did not differ among the groups (F(4, 84) = 1.494, p =.211). Conclusions: Our study indicates disorder-specific microbiome differences in patients with ASD. In future research on gut microbiota in neurodevelopmental disorders, it is necessary to consider the impact of ASD and ADHD co-occurrence, and strictly control for background information such as diet, to elucidate the gut–microbiota interaction in ASD and ADHD for exploring the potential of therapeutic interventions.

  • Access to Document (DOI)

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

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

Reviews, Commentaries, etc. 【 Display / hide 】

Reviews, Commentaries, etc. 【 Display / hide 】

• Challenges introduced by Japan's drug pricing policy

  Kinoshita S., Kishimoto T.

  The Lancet Regional Health - Western Pacific 51 2024.10

  • Access to Document (DOI)

• 【メンタルヘルス領域におけるオンライン診療・相談・支援】精神科領域におけるオンライン診療

  木下 翔太郎, 岸本 泰士郎

  心と社会 ((公財)日本精神衛生会)  55 ( 3 ) 8 - 14 2024.09

  ISSN  0023-2807

• 【睡眠障害についてかかりつけ医が知っておきたいこと】睡眠を計測するウェアラブルデバイス

  岸本 泰士郎, 北沢 桃子, 木下 翔太郎

  日本医師会雑誌 ((公社)日本医師会)  153 ( 5 ) 482 - 482 2024.08

  ISSN  0021-4493

• 関節リウマチ,シェーグレン症候群,SLE等の膠原病リウマチ性疾患に伴ううつ病　デジタルバイオマーカーの可能性も含めて

  泉 啓介, 岸本 泰士郎

  Depression Strategy ((株)先端医学社)  14 ( 3 ) 8 - 12 2024.07

  ISSN  2186-2575

• 音響学的解析と機械学習を用いた自由会話による認知症スクリーニング

  堀込 俊郎, 梁 國經, 岸本 泰士郎

  老年精神医学雑誌 ((株)ワールドプランニング)  35 ( 増刊II ) 242 - 242 2024.07

  ISSN  0915-6305

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Presentations 【 Display / hide 】

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)

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

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。

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Awards 【 Display / hide 】

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

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Courses Taught 【 Display / hide 】

Courses Taught 【 Display / hide 】

• LECTURE SERIES, PSYCHIATRY

  2024

• LECTURE SERIES, PSYCHIATRY

  2023

• LECTURE SERIES, PSYCHIATRY

  2022

• LECTURE SERIES, PSYCHIATRY

  2021

• LECTURE SERIES, PSYCHIATRY

  2020

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Academic Activities 【 Display / hide 】

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

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Memberships in Academic Societies 【 Display / hide 】

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

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Committee Experiences 【 Display / hide 】

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

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