著書 - 岸本　泰士郎

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年

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年01月

　概要を見る

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)

Large-Scale Dialog Corpus Towards Automatic Mental Disease Diagnosis

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

　概要を見る

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.

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年

　概要を見る

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.

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年10月

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年06月

担当範囲: 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年12月

人工知能・機械学習を用いた精神科診療の可能性

岸本 泰士郎 橋本 正弘, 藤田 卓仙,, 科学評論社, 2017年03月

担当範囲: 257-262

代表的な評価尺度

岸本 泰士郎 三村 將, 医学書院, 2017年03月

担当範囲: 314-323

評価尺度

岸本 泰士郎 三村 將 編集, 岸本泰士郎 著, 医学書院, 2017年03月

担当範囲: 45-47

強迫－強迫観念,強迫行為

岸本 泰士郎, 医学書院, 2017年03月

担当範囲: 126-129

ひきこもり

岸本 泰士郎 三村 將, 医学書院, 2017年03月

担当範囲: 130-132

強迫症，ためこみ症

岸本 泰士郎 三村 將, 医学書院, 2017年03月

担当範囲: 183-186

医療分野におけるビッグデータ並びにICT・AIの利活用の最新動向―プロジェクト「PROMPT」の概要

岸本 泰士郎, 公益財団法人 ヒューマンサイエンス振興財団, 2017年03月

担当範囲: 41-47

不安症・強迫症の疾患概念, 今日の精神疾患治療指針 第2版

岸本 泰士郎, 三村 將, 医学書院, 2016年10月

担当範囲: 160-165

岸本 泰士郎, 内田 裕之, 鈴木 健文, 三村 將 共訳. 「抗精神病薬の予防的投与」, 「抗精神病薬の併用療法」, 「陰性症状」, 「モニタリング」, 「有害事象の相対的評価概覧」, 「統合失調症の治療アルゴリズム」, 「第一世代抗精神病薬－治療における位置づけ」, 「統合失調症とオメガ３脂肪酸（魚油）」, 「統合失調症の新しい薬剤, 開発中の薬剤」, 「統合失調症治療のNICEガイドライン」, モーズレイ処方ガイドライン 第12版 上巻

ワイリー・パブリッシング・ジャパン株式会社, 2016年10月

担当範囲: 29-56

疾患ステージから見た統合失調症薬物療法：試験デザインの限界に関する考察を含めて, 臨床精神薬理

岸本 泰士郎, 星和書店, 2016年10月

統合失調症の再発をより確実に予防するために－メタ解析に基づく検討－

岸本 泰士郎, 東京こころのクリニック, 2016年08月

担当範囲: 16;142-151

統合失調症のリカバリーを見据えた薬物療法,精神科臨床 Legato

岸本 泰士郎, メディカルレビュー社, 2016年07月

担当範囲: 2(3);24-28

統合失調症のリカバリー達成に向けて薬物療法にできること,臨床精神薬理

岸本 泰士郎, 星和書店, 2016年03月

担当範囲: 19(3):259-265