KISHIMOTO Taishiro

写真a

Affiliation

School of Medicine, Hills Joint Research Laboratory for Future Preventive Medicine and Wellness (Shinanomachi)

Position

Project Professor (Non-tenured)

Related Websites

Profile Summary 【 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

  • 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

  • 慶應義塾大学, 慶応義塾大学, Coursework, 2009.02

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

 

Books 【 Display / hide

  • 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

  • 精神科の遠隔医療, 図説・日本の遠隔医療2017

    岸本 泰士郎, 一般社団法人 日本遠隔医療学会, 2017.12

  • 人工知能・機械学習を用いた精神科診療の可能性

    TAISHIRO KISHIMOTO, 科学評論社, 2017.03

    Scope: 257-262

  • 代表的な評価尺度

    TAISHIRO KISHIMOTO, 医学書院, 2017.03

    Scope: 314-323

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Papers 【 Display / hide

  • Association of electroconvulsive therapy-induced structural plasticity with clinical remission

    Takamiya A., Kishimoto T., Hirano J., Kikuchi T., Yamagata B., Mimura M.

    Progress in Neuro-Psychopharmacology and Biological Psychiatry (Progress in Neuro-Psychopharmacology and Biological Psychiatry)  110   110286 2021.08

    ISSN  02785846

     View Summary

    © 2021 Elsevier Inc. Background: Electroconvulsive therapy (ECT) is the most effective treatment for severe depression. Recent neuroimaging studies have consistently reported that ECT induces volume increases in widely distributed brain regions. However, it still remains unclear about ECT-induced volume changes associated with clinical improvement. Methods: Longitudinal assessments of structural magnetic resonance imaging were conducted in 48 participants. Twenty-seven elderly melancholic depressed individuals (mean 67.5 ± 8.1 years old; 19 female) were scanned before (TP1) and after (TP2) ECT. Twenty-one healthy controls were also scanned twice. Whole-brain gray matter volume (GMV) was analyzed via group (remitters, nonremitters, and controls) by time (TP1 and TP2) analysis of covariance to identify ECT-related GMV changes and GMV changes specific to remitters. Within-subject and between-subjects correlation analyses were conducted to investigate the associations between clinical improvement and GMV changes. Depressive symptoms were evaluated using the 17-item Hamilton Depression Rating Scale (HAM-D), and remission was defined as HAM-D total score ≤ 7. Results: Bilateral ECT increased GMV in multiple brain regions bilaterally regardless of clinical improvement. Remitters showed a larger GMV increase in the right-lateralized frontolimbic brain regions compared to nonremitters and healthy controls. GMV changes in the right hippocampus/amygdala and right middle frontal gyrus showed correlations with clinical improvement in within−/between-subjects correlation analyses. Conclusions: ECT-induced GMV increase in the right frontolimbic regions was associated with clinical remission.

  • Improvements in the degree of understanding the treatment guidelines for schizophrenia and major depressive disorder in a nationwide dissemination and implementation study

    Numata S., Nakataki M., Hasegawa N., Takaesu Y., Takeshima M., Onitsuka T., Nakamura T., Edagawa R., Edo H., Miura K., Matsumoto J., Yasui-Furukori N., Kishimoto T., Hori H., Tsuboi T., Yasuda Y., Furihata R., Muraoka H., Ochi S., Nagasawa T., Kyou Y., Murata A., Katsumoto E., Ohi K., Hishimoto A., Inada K., Watanabe K., Hashimoto R.

    Neuropsychopharmacology Reports (Neuropsychopharmacology Reports)   2021

     View Summary

    © 2021 The Authors. Neuropsychopharmacology Reports published by John Wiley & Sons Australia, Ltd on behalf of the Japanese Society of NeuropsychoPharmacology. Background: To implement clinical practice guidelines (CPGs), it is necessary for psychiatrists to deepen their understanding of the CPGs. The Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE) project is a nationwide dissemination and implementation study of two sets of CPGs for schizophrenia and major depressive disorder (MDD). Methods: A total of 413 psychiatrists (n = 212 in 2016; n = 201 in 2017) learned the two CPGs in the education program of the EGUIDE project, and clinical knowledge of these CPGs was evaluated at baseline and after the programs. To improve the correct answer rate for clinical knowledge after the programs, we revised the lecture materials associated with items that had a low correct answer rate in 2016 and used the revised lecture materials with the CPGs in 2017. The rates of correct answers after the programs between the 2016 and 2017 groups were compared. Results: The correct answer rate of one item on the schizophrenia CPG and one item on the MDD CPG tended to be improved (S-D5 and D-C6) and that of one on the MDD CPG was significantly improved (D-D3, P = 0.0008) in the 2017 group compared to those in the 2016 group. Conclusions: We reported improvements in clinical knowledge of CPGs after the EGUIDE program in the 2017 group following revision of the lecture materials based on results from the 2016 group. These attempts to improve the degree of understanding of CPGs may facilitate the successful dissemination and implementation of psychiatric guidelines in everyday practice.

  • Quantum Kinetic Equilibrium

    Chad T. Kishimoto, Heather Hodlin, Olexiy Dvornikov

     2020.11

     View Summary

    We solved the Quantum Kinetic Equations (QKEs) for an active-sterile neutrino
    system in the early universe. While on the surface this may seem to be an
    overly simplistic system, other linear two-state systems can be mapped onto the
    active-sterile system. In the early universe, we find that solutions to the
    QKEs are well described by an adiabatic approximation where the off-diagonal
    terms of the density operator are constant on short (oscillation and/or
    scattering) timescales, but may slowly evolve on long (expansion) timescales.
    This "quantum kinetic equilibrium" attains as the quantum development of phase
    balances with the kinetic destruction of phase. In this work, we introduce and
    assess this equilibrium ansatz as the neutrino states evolve in the early
    universe with a non-zero lepton number, engendering level crossings that result
    in scattering-induced active-sterile neutrino transformation.

  • Virtual reality exposure therapy for social anxiety disorder: A systematic review and meta-Analysis

    Horigome T., Kurokawa S., Sawada K., Kudo S., Shiga K., Mimura M., Kishimoto T.

    Psychological Medicine (Psychological Medicine)  50 ( 15 ) 2487 - 2497 2020.11

    ISSN  00332917

     View Summary

    Copyright © The Author(s) 2020. Published by Cambridge University Press. Background Virtual reality exposure therapy (VRET) is currently being used to treat social anxiety disorder (SAD); however, VRET's magnitude of efficacy, duration of efficacy, and impact on treatment discontinuation are still unclear. Methods We conducted a meta-Analysis of studies that investigated the efficacy of VRET for SAD. The search strategy and analysis method are registered at PROSPERO (#CRD42019121097). Inclusion criteria were: (1) studies that targeted patients with SAD or related phobias; (2) studies where VRET was conducted for at least three sessions; (3) studies that included at least 10 participants. The primary outcome was social anxiety evaluation score change. Hedges' g and its 95% confidence intervals were calculated using random-effect models. The secondary outcome was the risk ratio for treatment discontinuation. Results Twenty-Two studies (n = 703) met the inclusion criteria and were analyzed. The efficacy of VRET for SAD was significant and continued over a long-Term follow-up period: Hedges' g for effect size at post-intervention,-0.86 (-1.04 to-0.68); three months post-intervention,-1.03 (-1.35 to-0.72); 6 months post-intervention,-1.14 (-1.39 to-0.89); and 12 months post-intervention,-0.74 (-1.05 to-0.43). When compared to in vivo exposure, the efficacy of VRET was similar at post-intervention but became inferior at later follow-up points. Participant dropout rates showed no significant difference compared to in vivo exposure. Conclusion VRET is an acceptable treatment for SAD patients that has significant, long-lasting efficacy, although it is possible that during long-Term follow-up, VRET efficacy lessens as compared to in vivo exposure.

  • Safety/tolerability of antipsychotics in the treatment of adult patients with major depressive disorder: a systematic review and meta-analysis

    Hagi K., Kishimoto T., Kurokawa S., Correll C. U.

    EUROPEAN NEUROPSYCHOPHARMACOLOGY 40   S184 - S185 2020.11

    ISSN  0924-977X

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

Reviews, Commentaries, etc. 【 Display / hide

  • 【ビッグデータを活用した精神科薬物治療】人工知能技術を用いた最適な薬物選択に関する選択的レビュー

    木下 翔太郎, 桶屋 こむぎ, 岸本 泰士郎

    臨床精神薬理 ((株)星和書店)  24 ( 1 ) 21 - 26 2021.01

    ISSN  1343-3474

     View Summary

    抗精神病薬や抗うつ薬など、精神科薬物治療における選択肢は年々増えてきている。しかし、精神科領域においては、各疾患の発症メカニズムが完全にわかっていないこともあり、目の前の患者にとってどの薬剤が最も有効であるかを予測するのが難しいという問題がある。そのため、精神科薬物治療における治療効果予測は長年の研究テーマとなっている。近年では、膨大なデータからその特徴量を抽出する解析技術である機械学習を用いた研究も数多く報告されるようになってきており、脳画像や脳波などの検査結果や、遺伝子多型を分析したデータなどと組み合わせることにより、薬剤の治療効果や副作用の予測を行う試みがなされている。このような研究がさらに発展していくことで、個別の症例に合わせた薬剤選択が可能となることが期待されている。本稿では、機械学習を用いた新しい試みについて紹介する。(著者抄録)

  • 【神経疾患と人工知能(AI)】人工知能の精神科領域への応用

    米澤 賢吾, 木下 翔太郎, 岸本 泰士郎

    脳神経内科 ((有)科学評論社)  93 ( 6 ) 771 - 777 2020.12

    ISSN  2434-3285

  • Unmet needs of patients with major depressive disorder – Findings from the ‘Effectiveness of Guidelines for Dissemination and Education in Psychiatric Treatment (EGUIDE)’ project: A nationwide dissemination, education, and evaluation study

    Iida H., Iga J., Hasegawa N., Yasuda Y., Yamamoto T., Miura K., Matsumoto J., Murata A., Ogasawara K., Yamada H., Hori H., Ichihashi K., Hashimoto N., Ohi K., Yasui-Furukori N., Tsuboi T., Nakamura T., Usami M., Furihata R., Takaesu Y., Iwamoto K., Sugiyama N., Kishimoto T., Tsujino N., Yamada H., Hishimoto A., Nemoto K., Atake K., Muraoka H., Katsumoto E., Oishi S., Inagaki T., Ito F., Imamura Y., Kido M., Nagasawa T., Numata S., Ochi S., Iwata M., Yamamori H., Fujita J., Onitsuka T., Yamamura S., Makinodan M., Fujimoto M., Takayanagi Y., Takezawa K., Komatsu H., Fukumoto K., Tamai S., Yamagata H., Kubota C., Horai T., Inada K., Watanabe K., Kawasaki H., Hashimoto R.

    Psychiatry and Clinical Neurosciences (Psychiatry and Clinical Neurosciences)  74 ( 12 ) 667 - 669 2020.12

    ISSN  13231316

  • 【神経症候学と神経診断学-AIは味方か敵か?】特異的症状の症候学・診断学とAI うつ病

    中島 和樹, 岸本 泰士郎, 三村 將

    Clinical Neuroscience ((株)中外医学社)  38 ( 11 ) 1474 - 1476 2020.11

    ISSN  0289-0585

  • 【双極性障害-最新の診断と治療-】双極性障害の診療における機械学習の活用

    中島 和樹, 岸本 泰士郎

    日本臨床 ((株)日本臨床社)  78 ( 10 ) 1784 - 1789 2020.10

    ISSN  0047-1852

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Presentations 【 Display / hide

  • Perspectives in machine learning for predicting psychiatric conditions.

    TAISHIRO KISHIMOTO

    WFSBP 2018 KOBE (KOBE) , 2018.09, Oral Presentation(guest/special)

  • ICTや機械学習を活用した精神科領域における重症度評価の試み

    TAISHIRO KISHIMOTO

    がん分子修飾制御学分野主催セミナー (東京) , 2018.08, Oral Presentation(guest/special)

  • 情報処理技術(ICT)や機械学習を用いたうつ病診療の展望

    TAISHIRO KISHIMOTO

    第3回CNSサミット (東京) , 2018.08, Oral Presentation(guest/special)

  • 情報通信技術(ICT)や機械学習を用いたうつ病診療の展望

    TAISHIRO KISHIMOTO

    第3回CNSサミット, 2018.08, Oral Presentation(guest/special)

  • ビデオ会議システムを用いた遠隔認知行動療法の開発:認知行動療法の普及を (実践発表) 見据えて

    TAISHIRO KISHIMOTO

    第15回日本うつ病学会総会, 2018.07, Poster (general)

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

  • 令和3年度 医工連携・人工知能実装研究事業「リストバンド型ウェアラブルデバイスデータを用いてうつ病スクリーニングおよび重症度評価を可能とするソフトウェア医療機器の開発」

    2021
    -
    Present

    日本医療研究開発機構(AMED, Commissioned research, Principal Investigator

  • KGRI-IoT健康ライフ研究コンソーシアム「脳波を用いた認知機能評価基準の定量化」(研究代表者)

    2020
    -
    Present

    岸本泰士郎, Consortium, Principal Investigator

  • 障害者対策総合研究開発事業(精神障害分野)「対面診療に比したオンライン診療の非劣勢試験:COVID-19によって最も影響を受け得る精神疾患に対するマスタープロトコル試験による検証」

    2020
    -
    Present

    日本医療研究開発機構(AMED), 岸本泰士郎, Commissioned research, Principal Investigator

  • 障害者対策総合研究開発事業(精神障害分野)「認知行動療法の治療最適化ツールと客観的効果判定指標の開発」

    2019
    -
    Present

    日本医療研究開発機構(AMED), 中川 敦夫, 岸本泰士郎, Commissioned research, Co-investigator

  • 戦略的創造研究推進事業(CREST)「精神医学×メディア解析技術による心の病の定量化・早期発見と社会サービスの創出」

    2019
    -
    Present

    国立研究開発法人科学技術振興機構(JST), 佐藤 真一, 岸本泰士郎, Commissioned research, Co-investigator

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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

  • Japanese Society of Neuropsychopharmacology Excellent Presentation Award for CINP 2014

    2014.06, CINP 2014

  • 国際学会発表賞

    2014.06, 日本精神神経科学会

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Courses Taught 【 Display / hide

  • LECTURE SERIES, PSYCHIATRY

    2021

  • LECTURE SERIES, PSYCHIATRY

    2020

  • LECTURE SERIES, PSYCHIATRY

    2019

 

Social Activities 【 Display / hide

  • 厚生労働省 平成25年度 障害者総合福祉推進事業 「精神病床に入院している難治性患者の地域移行の推進に向けた支援の在り方に関する実態調査」研究分担者・実務担当者

    2014
    -
    Present

Memberships in Academic Societies 【 Display / hide

  • 保健医療分野におけるAI活用推進懇談会 構成員, 

    2017
    -
    Present
  • 独立行政法人医薬品医療機器総合機構専門委員, 

    2017
    -
    2019
  • 第14回日本うつ病学会総会プログラム委員, 

    2016
    -
    Present
  • 日本総合病院精神医学会 会員 (2016~ プログラム委員), 

    2015
    -
    Present
  • 日本遠隔医療学会 (2016~ 精神科分科会代表), 

    2014
    -
    Present

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Committee Experiences 【 Display / hide

  • 2018.08
    -
    2019.03

    プログラム委員, 第41回日本生物学精神医学会

  • 2018.08
    -
    2019.03

    委員, 総務省 オンライン診療の普及促進に向けたモデル構築にかかる調査研究検討委員会

  • 2018.05
    -
    Present

    評議員, 一般社団法人日本メディカルAI学会

  • 2017
    -
    Present

    構成員, 保健医療分野におけるAI活用推進懇談会

  • 2017
    -
    2019

    専門委員, 独立行政法人医薬品医療機器総合機構

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