Kizaki, Hayato

写真a

Affiliation

Faculty of Pharmacy, Department of Pharmacy 医薬品情報学講座 ( Shiba-Kyoritsu )

Position

Research Associate/Assistant Professor/Instructor

Career 【 Display / hide 】

  • 2018.11
    -
    Present

    慶應義塾大学薬学部, 医薬品情報学講座, 助教

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Academic Background 【 Display / hide 】

  • 2010.04
    -
    2014.03

    The University of Tokyo, 薬学部, 薬科学科

    University, Graduated

  • 2014.04
    -
    2016.03

    The University of Tokyo, 薬学系研究科, 薬科学専攻

    Graduate School, Completed, Master's course

  • 2016.09

    The University of Tokyo, 薬学系研究科, 薬科学専攻

    Graduate School, Doctoral course

  • 2017.09

    The University of Tokyo, 薬学系研究科, 薬学専攻

    Graduate School, Doctoral course

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Academic Degrees 【 Display / hide 】

  • 博士（薬科学）, The University of Tokyo, Dissertation, 2024.10

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Licenses and Qualifications 【 Display / hide 】

  • 東京大学フューチャーファカルティプログラム修了, 大学教員としてのキャリアを進むにあたり不可欠となる教育力の向上をめざすプログラム, 2017.03

  • 薬剤師免許, 2019

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Research Areas 【 Display / hide 】

  • Life Science / Clinical pharmacy

  • Life Science / Medical management and medical sociology

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Research Keywords 【 Display / hide 】

  • 介護施設

  • 医療安全

  • 医薬品情報

  • 多職種連携

  • 薬剤師

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Papers 【 Display / hide 】

  • A scalable natural language processing framework for drug repurposing in chemotherapy-induced adverse events from clinical narrative records

    Tsuchiya M., Inoue M., Kawazoe Y., Shimamoto K., Seki T., Imai S., Kizaki H., Shinohara E., Yada S., Wakamiya S., Aramaki E., Hori S.

    European Journal of Cancer 232   116157 2026.01

    ISSN  09598049

    　View Summary

    Background Preventing chemotherapy-related adverse events (AEs) remains an unmet clinical challenge. Preclinical studies have suggested protective effects of several existing agents, but translation into human evidence has been limited. We aimed to establish proof of concept (PoC) for drug repurposing by applying a natural language processing (NLP)-based framework to electronic health record (EHR) narratives, thereby bridging preclinical findings with clinical validation. Methods We retrospectively analyzed 56,326 patients with cancer treated at the University of Tokyo Hospital (2004–2023). A transformer-based NLP model extracted symptomatic AEs from clinical notes. Candidate preventive drugs identified from preclinical evidence were assessed using propensity score matching and Cox proportional hazards models. We evaluated angiotensin II receptor blockers (ARBs) for fluoropyrimidine-induced oral mucositis and ramelteon for platinum-induced peripheral neuropathy, with laxatives serving as a negative control. Results NLP demonstrated high accuracy (precision 0.81–0.83; recall 0.95–0.97). After matching, ARB co-administration was significantly associated with reduced mucositis incidence (hazard ratio [HR] 0.58, 95 % confidence interval [CI] 0.44–0.77; P < 0.001), representing a clinical PoC consistent with mechanistic preclinical data. Ramelteon showed an exploratory protective signal against neuropathy (HR 0.60, 95 % CI:0.38–0.93; P = 0.024). No preventive association was observed for laxatives. Conclusions This study introduces a scalable NLP-epidemiology framework for non-invasive, real-world validation of drug repurposing candidates. The ARB finding provides human-level PoC evidence supporting prospective clinical testing, while the ramelteon signal warrants further exploration. Our approach demonstrates how EHR narratives can operationalize translational research, prioritizing safe, accessible agents for improving the tolerability of cancer treatment.

  • A real-world pharmacovigilance study of adverse events associated with esketamine: disproportionality analysis and detection of potential drug-drug interaction signals

    Pisanu C., Imai S., Tsuchiya M., Inoue M., Ikegami K., Zammarchi G., Kizaki H., Hori S.

    European Journal of Clinical Pharmacology 82 ( 1 ) 13 2026.01

    ISSN  00316970

    　View Summary

    Purpose: We conducted a comprehensive analysis of esketamine-related adverse events (AE) on the FDA Adverse Event Reporting System (FAERS) database, taking into account for the first time drug-drug interaction signals. Methods: We conducted a retrospective case/non-case study of esketamine-related AEs reported in the FAERS database up to the last quarter (Q4) of 2024. Potential signals were detected using the reporting odds ratio (ROR) and confidence intervals (CI), while drug-drug interactions were studied using different metrics such as lift, conviction and the combination risk ratio detection algorithm. An analysis of sex differences was also performed using the relative ROR and CI. Results: The analysis of 7,790 reports in which esketamine was a primary or secondary suspect identified potential safety signals for 173 AEs. Novel signals include homicidal ideation (ROR = 5.30, 95% CI: 2.38-11.82) and substance use disorder (ROR = 6.12, 95% CI: 2.54-14.73). Women showed a longer time to onset than men (p = 0.003). In addition, we detected sex differences in 23 AEs, seven of which were more likely to be reported in women, while 16 in men. Among these, four were significant exclusively in women (oxygen saturation decreased, abnormal behaviour, unresponsive to stimuli and aggression) and two in men (vision blurred and bradycardia). Potential signals of additive and multiplicative drug-drug interactions were detected for antidepressants (venlafaxine for“dizziness” and bupropion for “agitation”) and antipsychotics (risperidone for “vertigo”). Conclusions: Our results increase knowledge on potential risks related to esketamine AEs and potential drug-drug interaction signals in a real-world setting.

  • A questionnaire survey of healthcare access and dietary habits in a rural Japanese community: implications for potential community pharmacy roles.

    Kizaki H, Tsukamoto M, Yamada M, Ito S, Sasaki M, Sakamoto K, Ikeda Y, Miyamoto K, Iino H, Hori S

    Journal of pharmaceutical health care and sciences  2025.12

    ISSN  2055-0294

  • Analysis of factors affecting difficulty in handling oral medicine using electronic medication notebook-based personal health records

    Shimizu Y., Tsuchiya M., Asano M., Imai S., Kizaki H., Ito Y., Tsuchiya M., Kuriyama R., Yoshida N., Shimada M., Sando T., Ishijima T., Hori S.

    Scientific Reports 15 ( 1 ) 26867 2025.12

    　View Summary

    Tablets and capsules are widely used forms of oral medication, but some patients experience difficulty handling them, which can reduce medication adherence and affect health outcomes. This study aimed to identify factors contributing to perceived handling difficulty, using data from harmo<sup>®</sup>, a nationwide electronic medication notebook system. A questionnaire was distributed to adult users who had been prescribed oral medications, and the responses were linked with personal health records to analyze medication characteristics and patient backgrounds. Among the 1,230 respondents, 24% reported difficulty with small tablets or capsules. A size threshold was identified: a combined long and short diameter of 13.3 mm or less was most associated with handling problems (ROC-AUC = 0.834). Binomial logistic regression analysis revealed that difficulty in applying force with the hands (OR = 2.64), prescription of small tablets or capsules (OR = 2.52), and medical histories of hypertension (OR = 1.69) and osteoporosis (OR = 4.99) were significantly associated with reported difficulty. These results suggest that both the physical characteristics of formulations and individual patient factors influence medication usability. Our results provide evidence to inform more patient-centered approaches to oral formulation design and prescribing practices, ultimately supporting better adherence and medication safety.

  • A patient-centered approach to developing and validating a natural language processing model for extracting patient-reported symptoms

    Watabe S., Yanagisawa Y., Sayama K., Yokoyama S., Someya M., Taniguchi R., Yada S., Aramaki E., Kizaki H., Tsuchiya M., Imai S., Hori S.

    Scientific Reports 15 ( 1 ) 27652 2025.12

    　View Summary

    Patient-reported symptoms provide valuable insights into patient experiences and can enhance healthcare quality; however, effectively capturing them remains challenging. Although natural language processing (NLP) models have been developed to extract adverse events and symptoms from medical records written by healthcare professionals, limited studies have focused on models designed for patient-generated narratives. This study developed an NLP model to extract patient-reported symptoms from pharmaceutical care records and validated its effectiveness in analyzing diverse patient-generated narratives. The target dataset comprised “Subjective” sections of pharmaceutical care records created by community pharmacists for patients prescribed anticancer drugs. Two annotation guidelines were applied to develop robust ground-truth data, which was used to develop and evaluate a new transformer-based named entity recognition model. Model performance was compared with that of an existing tool for Japanese clinical texts and tested on external patient-generated blog data to evaluate generalizability. The newly developed BERT-CRF model significantly outperformed the existing model, achieving an F1 score > 0.8 on pharmaceutical care records and extracting > 98% of physical symptom entries from patient blogs, with a 20% improvement over the existing tool. These findings highlight the importance of fine-tuning models using patient-specific narrative data to capture nuanced and colloquial symptom expressions.

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

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Reviews, Commentaries, etc. 【 Display / hide 】

  • 新薬まるわかり アウィクリ注フレックスタッチ総量300単位/700単位 (インスリンイコデク)

    木﨑速人，佐藤宏樹，三木晶子著　堀 里子，澤田康文監.

    日経ドラッグインフォメーション ( 日経BP社)  329 2025.03

    Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Joint Work

  • 新薬まるわかり フォゼベル 5mg/10mg/20mg/30mg（テナパノル塩酸塩）

    木﨑速人，平井理夏，佐藤宏樹，三木晶子著　堀 里子，澤田康文監.

    日経ドラッグインフォメーション ( 日経BP社)  327 2025.01

    Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Joint Work

  • 新薬まるわかり レクビオ皮下注 300 mg シリンジ（インクリシランナトリウム）

    木﨑速人，清水海人，佐藤宏樹，三木晶子著　堀 里子，澤田康文監.

    日経ドラッグインフォメーション ( 日経BP社)  325 2024.11

    Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Joint Work

  • 新薬まるわかり リットフーロカプセル 50mg（リトレシチニブトシル酸塩）

    木﨑速人，出雲真帆，佐藤宏樹，三木晶子著　堀 里子，澤田康文監.

    日経ドラッグインフォメーション ( 日経BP社)  323 2024.09

    Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Joint Work

  • 新薬まるわかり マンジャロ皮下注2.5mg/5mg/7.5mg/10mg/12.5mg/15mgアテオス（チルゼパチド）

    木﨑速人，出雲真帆，佐藤宏樹，三木晶子著　堀 里子，澤田康文監.

    日経ドラッグインフォメーション ( 日経BP社)  321 2024.07

    Article, review, commentary, editorial, etc. (trade magazine, newspaper, online media), Joint Work

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Presentations 【 Display / hide 】

  • Development of an Automated Classification System for Medication-Related Incident Factors: A Practical Approach to Enhancing Patient Safety Management.

    Takamatsu Y, Ebara S, Kizaki H, Watabe S, Imai S, Yada S, Aramaki E, Yasumuro O, Funakoshi R, Hori S.

    [International presentation]  Medinfo 2025, 

    2025.08
    , 

    Oral presentation (general)

  • Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis. Stud Health Technol Inform.

    Tsuchiya M, Shimamoto K, Kawazoe Y, Shinohara E, Yada S, Wakamiya S, Imai S, Kizaki H, Hori S, Aramaki E.

    [International presentation]  Medinfo 2025, 

    2025.08
    , 

    Oral presentation (general)

  • マスメディアの「医薬品」に関する報道が患者に及ぼす影響

    佐々木愛、今井俊吾、阿部真也、松井　洸、小野貴登、卯田健太、佐山杏子、木﨑速人、山口　浩、堀　里子、野村和彦

    [Domestic presentation]  第27回日本医薬品情報学会総会・学術大会, 

    2025.07
    , 

    Oral presentation (general)

  • 課題研究班実施時の経験紹介1　～大学院生/大学教員として携わった立場から

    木﨑速人

    [Domestic presentation]  第27回日本医薬品情報学会総会・学術大会, 

    2025.07
    , 

    Symposium, workshop panel (public)

  • 自然言語処理を用いた類似インシデント事例検索システムの開発

    久村颯海、木﨑速人、土屋雅美、今井俊吾、西山智弘、矢田竣太郎、荒牧英治、安室修、舟越亮寛、堀　里子

    [Domestic presentation]  日本医療薬学会 第8回 フレッシャーズ・カンファランス, 

    2025.06
    , 

    Oral presentation (general)

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

  • 大規模言語モデル（LLM）活用による薬学生の省察的実践を促す対話型学習支援システムの開発

    2025.12
    -
    2026.11

    公益財団法人カシオ科学振興財団, Principal investigator

  • Research on Multimodal Analysis of Voice and Language Information in Medication Counseling and its Application to Effective Guidance Methods and Educational Support

    2025.04
    -
    2026.03

    British Council, RENKEI workshop award grants, Rafael MESTRE, Research grant, Collaborating Investigator(s) (not designated on Grant-in-Aid)

    　View Remarks

    配分額は日本円の概算額（Full amount of 6000GBP）

  • 薬局におけるPHR活用方法とその推進に関する実証的研究

    2025.04
    -
    2027.03

    厚生労働科学研究費補助金, 医薬品・医療機器等レギュラトリーサイエンス政策研究事業, Research grant, Coinvestigator(s)

  • Establishment of a foundation for optimizing risk management of medical incidents based on collaboration between non-medical and medical professionals

    2024.04
    -
    2027.03

    Grants-in-Aid for Scientific Research, Grant-in-Aid for Scientific Research (C), Principal investigator

    　View Summary

    介護場面での患者安全の実現のためには，介護者（非医療専門家）や医療者が発信する患者情報に基づくリスク管理の最適化が重要である．本研究では，こうした情報（主にテキスト情報）を活用して，医療インシデントのリスク管理において重要な情報を抽出する自然言語処理（Natural Language Processing, NLP）モデルを開発する．ここで得たNLPモデルを，リスク管理における重要情報の抽出と集約に活用し，介護職と医療職の連携に基づくリスク管理の最適化を促すシステム構築に取り組む．本研究は，介護関連情報の利活用を推進させるとともに，介護場面における医療インシデントのリスク管理の最適化に大きく貢献することが期待される．

  • 要介護等高齢者の薬物治療適正化・医療安全確保に向けた介護施設における医薬品関連インシデント事例の要因解析

    2019.04
    -
    2020.03

    日本医薬品情報学会, 課題研究班, No Setting, Principal investigator

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Awards 【 Display / hide 】

  • 第35回日本医療薬学会年会 優秀演題賞

    木﨑速人，吉川康大，河野広太郎，上村忠聖，鈴木信也，佐村　優，廣瀬直樹，永野靖典，林　誠一，土屋雅美，今井俊吾，堀　里子, 2025.11, 日本医療薬学会, 濁音・半濁音を考慮した新規薬名類似度指標の開発と調剤場面での薬剤取り違えリスク評価への応用検討

    Type of Award： Award from Japanese society, conference, symposium, etc.

  • 第35回日本医療薬学会年会 優秀演題賞

    三星　知，今井俊吾，木崎速人，堀　里子, 2025.11, 日本医療薬学会, ランソプラゾールとセフトリアキソンの併用に伴う心室性不整脈および心停止リスク-JMDC医療機関データベースの解析

    Type of Award： Award from Japanese society, conference, symposium, etc.

  • 第19回日本ファーマシューティカルコミュニケーション学会大会 優秀演題賞

    薬剤師に相談するための質問リスト（QPLP）の活用可能性に関する患者へのインターネットパネル調査, 2025.09, 早川雅代、木﨑速人、土屋雅美、柳澤友希、今井俊吾、鈴木信行、香川由美、堀　里子

    Type of Award： Award from Japanese society, conference, symposium, etc.

  • Best Paper Award at MedInfo 2025

    Masami Tsuchiya, Kiminori Shimamoto, Yoshimasa Kawazoe, Emiko Shinohara, Shuntaro Yada, Shoko Wakamiya, Shungo Imai, Hayato Kizaki, Satoko Hori, Eiji Aramaki, 2025.08, Natural Language Processing-Based Approach to Detect Common Adverse Events of Anticancer Agents from Unstructured Clinical Notes: A Time-to-Event Analysis

    Type of Award： Award from international society, conference, symposium, etc.

  • 2024年度 日本医薬品情報学会 論文賞

    森部詩月，今井俊吾，佐山杏子，上村忠聖，林　誠一，木﨑速人，堀　里子, 2025.07, 薬名類似に起因する薬剤誤処方の傾向分析 ‐薬剤師による誤調剤事例との比較‐

    Type of Award： Honored in official journal of a scientific society, scientific journal

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Courses Taught 【 Display / hide 】

  • STUDY OF MAJOR FIELD: (EVALUATION AND ANALYSIS OF DRUG INFORMATION)

    2025

  • SEMINAR: (EVALUATION AND ANALYSIS OF DRUG INFORMATION)

    2025

  • RESEARCH FOR BACHELOR'S THESIS 1

    2025

  • PRE-CLINICAL TRAINING FOR HOSPITAL & COMMUNITY PHARMACY

    2025

  • PHARMACEUTICAL-ENGLISH SEMINAR

    2025

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Courses Previously Taught 【 Display / hide 】

  • 実務実習事前学習（実習）

    Keio University

    2018.04
    -
    2019.03
    , 

    Autumn Semester, Laboratory work/practical work/exercise, 160people

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Educational Activities and Special Notes 【 Display / hide 】

  • 明治大学 「教職実践演習」：「教育実習の総まとめ」、授業題目：正しい薬の育て方

    2017.11

    , Special Affairs

  • 東京大学教養学部 全学自由研究ゼミナール「伝えるを学ぼう」：第6回「大学院生による模擬授業・検討・解説３」、授業題目：創る薬から育てる薬へ

    2017.05

    , Special Affairs

  • 学校法人河合塾 知の追究講座 講師：「薬の創り方・育て方〜薬学研究の最前線〜」

    2017.04

    , Special Affairs

  • 東京大学文学部 第1回留学生ワークショップ 講師：「何気ない日本人の習慣・考え方を学ぼう！」

    2017.03

    , Special Affairs

  • 学校法人河合塾 学びみらいプログラム 講師：「正しい薬の育て方」

    2017.03

    , Special Affairs

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Memberships in Academic Societies 【 Display / hide 】

  • 日本薬学会, 

    2020
    -
    Present

  • 医薬品情報学会

    　

  • 医療薬学会

    　
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Committee Experiences 【 Display / hide 】

  • 2020.04
    -
    Present

    研究企画委員会 委員, 一般財団法人　日本医薬品情報学会

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