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
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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 】

  • オンラインでの健康相談の役割と課題：消費者視点からの分析

    大上 愛由，木﨑 速人，今井 俊吾，堀 里子

    医療薬学 51 ( 6 ) 335 - 344 2025.06

    Research paper (scientific journal), Joint Work, Accepted

  • 医療機関に所属する臨床研究コーディネーターのレジリエンス要因

    野呂 幾久子, 飯岡 緒美, 堀 里子, 中野 重行, 木﨑 速人, 石崎 雅人

    臨床薬理 ((一社)日本臨床薬理学会)  56 ( 2 ) 97 - 108 2025.05

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  03881601

    　View Summary

    It has been observed that CRCs, pivotal figures in clinical research, often grapple with a substantial mental burden. This study, which investigates the resilience factors of CRCs, aims to provide practical suggestions for their education and resilience development, empowering them with actionable knowledge. Ten CRCs, who had been working as CRCs for many years, were interviewed individually. They were asked about their challenging experiences while working as CRCs, how they successfully adapted to those challenges, and the factors involved in the process. We then inductively coded the factors involved in successfully adapting to challenges as the analysis theme. The qualitative analysis reveals that the factors of resilience of CRCs were classified into four groups(: 1)individual characteristics and ways of thinking,(2)meaning, goals, and motivation,(3)ways of dealing with work, and(4)support from those around them. Among the factors, the following were considered to be particularly important for CRCs: meaning, continuing attempt to find meaning in one's work, even in the face of adversity; metacognition, handling reflection on one's own and others' cognitive activities from a bird's-eye view; and balancing professional and patient perspectives that maintains the rigor of carrying out clinical research as a medical professional with empathy for patients' feelings.

  • Incidence Status and Factors Associated With Tyrosine Kinase Inhibitor-Induced Hypertension in Patients With Renal Cell Carcinoma

    Nakanishi S., Ikegami K., Imai S., Kizaki H., Hori S.

    Cancer Reports 8 ( 5 ) e70219 2025.05

    Research paper (scientific journal), Joint Work, Accepted

    　View Summary

    Background: Although hypertension is a common side effect of tyrosine kinase inhibitor (TKI) treatment for renal cell carcinoma (RCC), there is limited evidence regarding its occurrence and related risk factors. Preliminary studies suggest that proton pump inhibitors (PPIs) may mitigate the risk of TKI-induced hypertension; however, their clinical effectiveness remains unclear. Aims: In this study, we examined the prevalence of TKI-induced hypertension and the patterns of antihypertensive prescriptions among patients with RCC in Japan. Additionally, we investigated factors associated with TKI-induced hypertension to assess the potential impact of PPIs. Methods and Results: Data from patients diagnosed with RCC who were prescribed TKIs between April 2008 and July 2021 were retrospectively gathered from a Japanese administrative database. TKI-induced hypertension was detected following the diagnosis of hypertension and subsequently the prescription of an antihypertensive agent during TKI therapy. The prescription details for antihypertensive agents were organized in a tabular format. Cox proportional hazards regression analysis was conducted to examine factors contributing to TKI-induced hypertension. Among the 225 patients analyzed, 36.4% experienced hypertension, and calcium channel blockers were the most prescribed antihypertensive agents. Pre-existing hypertension was identified as a risk factor for TKI-induced hypertension, while the concurrent use of PPIs did not show a tendency to reduce the risk of TKI-induced hypertension. Conclusions: These findings indicate the importance of blood pressure management in patients with elevated baseline blood pressure.

  • Accuracy of Diagnostic Coding for Acute Kidney Injury in Japan-Analysis of a Japanese Hospital-Based Database.

    Mitsuboshi S, Imai S, Tsuchiya M, Kizaki H, Hori S

    Pharmacoepidemiology and drug safety 34 ( 4 ) e70146 2025.04

    Research paper (scientific journal), Joint Work, Accepted,  ISSN  1053-8569

    　View Summary

    Purpose: To evaluate the accuracy of diagnostic coding for acute kidney injury (AKI) in Japan. Methods: The data analyzed were obtained from the JMDC hospital-based administrative claims database from cases registered between April 2014 and August 2022. Only patients who underwent serum creatinine measurements two or more times with intervals of 7 days or less were eligible for inclusion. AKIs were identified by International Classification of Diseases 10th Revision (ICD-10) codes N14 and N17. These were assessed according to the Kidney Disease: Improving Global Outcomes (KDIGO) criteria. Results: A total of 467 019 patients (median age, 74 [range, 20–99] years; male, 50.9%) were eligible for inclusion. Among these patients, 1849 (0.4%) were assigned ICD-10 codes for AKI. Among these 1849 patients, the code was assigned within 7 days of the occurrence of AKI (as defined by the KDIGO criteria) in 212 patients, within 14 days in 294 patients, and within 30 days in 386 patients. The positive predictive values and 95% confidence intervals of the ICD-10 code for AKI at these timepoints were as follows: within 7 days, 11.5% (10.1%–13.0%); within 14 days, 15.9% (14.3%–17.6%); and within 30 days, 20.9% (19.1%–22.8%). Conclusions: The ICD-10 codes for AKI showed poor positive predictive values for AKI as defined by the KDIGO criteria, suggesting that it may be difficult to identify AKI using ICD-10 codes alone in the Japanese context.

  • Improving Systematic Review Updates With Natural Language Processing Through Abstract Component Classification and Selection: Algorithm Development and Validation.

    Hasegawa T, Kizaki H, Ikegami K, Imai S, Yanagisawa Y, Yada S, Aramaki E, Hori S

    JMIR medical informatics 13   e65371 2025.03

    Research paper (scientific journal), Joint Work, Accepted

    　View Summary

    Background: A challenge in updating systematic reviews is the workload in screening the articles. Many screening models using natural language processing technology have been implemented to scrutinize articles based on titles and abstracts. While these approaches show promise, traditional models typically treat abstracts as uniform text. We hypothesize that selective training on specific abstract components could enhance model performance for systematic review screening. Objective: We evaluated the efficacy of a novel screening model that selects specific components from abstracts to improve performance and developed an automatic systematic review update model using an abstract component classifier to categorize abstracts based on their components. Methods: A screening model was created based on the included and excluded articles in the existing systematic review and used as the scheme for the automatic update of the systematic review. A prior publication was selected for the systematic review, and articles included or excluded in the articles screening process were used as training data. The titles and abstracts were classified into 5 categories (Title, Introduction, Methods, Results, and Conclusion). Thirty-one component-composition datasets were created by combining 5 component datasets. We implemented 31 screening models using the component-composition datasets and compared their performances. Comparisons were conducted using 3 pretrained models: Bidirectional Encoder Representations from Transformer (BERT), BioLinkBERT, and BioM- Efficiently Learning an Encoder that Classifies Token Replacements Accurately (ELECTRA). Moreover, to automate the component selection of abstracts, we developed the Abstract Component Classifier Model and created component datasets using this classifier model classification. Using the component datasets classified using the Abstract Component Classifier Model, we created 10 component-composition datasets used by the top 10 screening models with the highest performance when implementing screening models using the component datasets that were classified manually. Ten screening models were implemented using these datasets, and their performances were compared with those of models developed using manually classified component-composition datasets. The primary evaluation metric was the F10-Score weighted by the recall. Results: A total of 256 included articles and 1261 excluded articles were extracted from the selected systematic review. In the screening models implemented using manually classified datasets, the performance of some surpassed that of models trained on all components (BERT: 9 models, BioLinkBERT: 6 models, and BioM-ELECTRA: 21 models). In models implemented using datasets classified by the Abstract Component Classifier Model, the performances of some models (BERT: 7 models and BioM-ELECTRA: 9 models) surpassed that of the models trained on all components. These models achieved an 88.6% reduction in manual screening workload while maintaining high recall (0.93). Conclusions: Component selection from the title and abstract can improve the performance of screening models and substantially reduce the manual screening workload in systematic review updates. Future research should focus on validating this approach across different systematic review domains.

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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

  • 診療記録に自然言語処理を用いたCape誘発HFSに対するセレコキシブの予防効果の検証

    土屋 雅美, 河添 悦昌, 嶋本 公徳, 関 倫久, 今井 俊吾, 木崎 速人, 篠原 恵美子, 矢田 竣太郎, 若宮 翔子, 荒牧 英治, 堀 里子

    日本癌治療学会学術集会抄録集 ((一社)日本癌治療学会)  62回   O53 - 3 2024.10

  • 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

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Presentations 【 Display / hide 】

  • 診療記録を活用したフッ化ピリミジン系抗がん薬誘発性口内炎に対するAT2受容体拮抗薬の予防効果の検証

    井上真理, 土屋雅美, 嶋本公徳, 河添悦昌, 篠原恵美子, 矢田竣太郎, 若宮翔子, 今井俊吾, 木﨑速人, 堀 里子, 荒牧英治

    第34回日本医療薬学会年会, 

    2024.11
    , 

    Oral presentation (general)

  • 薬局利用者から薬剤師への質問促進リスト（QPLP）の開発：患者対象フォーカスグループインタビューと修正デルファイ法による検討

    早川雅代, 木﨑速人, 柳澤友希, 鈴木信行, 香川由美, 佐山杏子, 今井俊吾, 堀 里子

    第34回日本医療薬学会年会, 

    2024.11
    , 

    Oral presentation (general)

  • 自然言語処理を用いた診療テキストデータからのがん薬物療法副作用検出と可視化手法の提案

    土屋雅美, 嶋本公徳, 河添悦昌, 篠原恵美子, 矢田竣太郎, 若宮翔子, 今井俊吾, 木﨑速人, 堀 里子, 荒牧英治

    第34回日本医療薬学会年会, 

    2024.11
    , 

    Poster presentation

  • 自然言語処理技術を活用した薬局薬歴の症状聴取に基づくアセスメント内容の可視化

    柳澤友希, 渡部 哲, 横山さくら, 木﨑速人, 今井俊吾, 染谷光洋, 谷口亮央, 矢田竣太郎, 荒牧英治, 堀 里子

    第34回日本医療薬学会年会, 

    2024.11
    , 

    Poster presentation

  • 高用量メトトレキサート誘発性肝機能障害に対するアンジオテンシンII受容体拮抗薬の予防効果の検証

    宮田有優, 今井俊吾, 木﨑速人, 土屋雅美, 堀 里子

    第34回日本医療薬学会年会, 

    2024.11
    , 

    Oral presentation (general)

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

  • 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 】

  • 2025年度 日本医療薬学会 Postdoctoral Award

    木﨑速人, 2025, 日本医療薬学会, 介護施設の非医療者が経験する薬剤関連インシデントの分析基盤の構築に関する研究

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

  • 第34回日本医療薬学会年会 優秀演題賞

    井上真理, 土屋雅美, 嶋本公徳, 河添悦昌, 篠原恵美子, 矢田竣太郎, 若宮翔子, 今井俊吾, 木崎速人, 堀 里子, 荒牧英治, 2024.11, 第34回日本医療薬学会年会 , 診療記録を活用したフッ化ピリミジン系抗がん薬誘発性口内炎に対するAT2受容体拮抗薬の予防効果の検証

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

  • 日本医療薬学会 第7回 フレッシャーズ・カンファランス優秀演題発表賞

    板倉理子、木﨑速人、岡澤優太、今井俊吾、堀　里子, 2024.06, お薬手帳の活用推進を主目的としたすごろく学習プログラムの開発と実践

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

  • 日本薬学会第144年会学生優秀発表賞

    齊藤愛実、今井俊吾、木﨑速人、堀　里子, 2024.03, 診療情報データベースを用いたバンコマイシンによる腎機能障害の新規予防薬の探索

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

  • 日本薬学会第144年会学生優秀発表賞

    長谷川樹、矢田竣太朗、木﨑速人、今井俊吾、荒牧英治、堀　里子, 2024.03, 機械学習を用いたシステマティックレビュー更新における自動文献精査モデル実装時の論文アブストラクト要素選択の重要性の検討

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

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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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