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 】

  • 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

    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

    　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.

  • Identifying Adverse Events in Outpatients With Prostate Cancer Using Pharmaceutical Care Records in Community Pharmacies: Application of Named Entity Recognition.

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

    JMIR cancer 11   e69663 2025.03

    　View Summary

    Background: Androgen receptor axis-targeting reagents (ARATs) have become key drugs for patients with castration-resistant prostate cancer (CRPC). ARATs are taken long term in outpatient settings, and effective adverse event (AE) monitoring can help prolong treatment duration for patients with CRPC. Despite the importance of monitoring, few studies have identified which AEs can be captured and assessed in community pharmacies, where pharmacists in Japan dispense medications, provide counseling, and monitor potential AEs for outpatients prescribed ARATs. Therefore, we anticipated that a named entity recognition (NER) system might be used to extract AEs recorded in pharmaceutical care records generated by community pharmacists. Objective: This study aimed to evaluate whether an NER system can effectively and systematically identify AEs in outpatients undergoing ARAT therapy by reviewing pharmaceutical care records generated by community pharmacists, focusing on assessment notes, which often contain detailed records of AEs. Additionally, the study sought to determine whether outpatient pharmacotherapy monitoring can be enhanced by using NER to systematically collect AEs from pharmaceutical care records. Methods: We used an NER system based on the widely used Japanese medical term extraction system MedNER-CR-JA, which uses Bidirectional Encoder Representations from Transformers (BERT). To evaluate its performance for pharmaceutical care records by community pharmacists, the NER system was first applied to 1008 assessment notes in records related to anticancer drug prescriptions. Three pharmaceutically proficient researchers compared the results with the annotated notes assigned symptom tags according to annotation guidelines and evaluated the performance of the NER system on the assessment notes in the pharmaceutical care records. The system was then applied to 2193 assessment notes for patients prescribed ARATs. Results: The F1-score for exact matches of all symptom tags between the NER system and annotators was 0.72, confirming the NER system has sufficient performance for application to pharmaceutical care records. The NER system automatically assigned 1900 symptom tags for the 2193 assessment notes from patients prescribed ARATs; 623 tags (32.8%) were positive symptom tags (symptoms present), while 1067 tags (56.2%) were negative symptom tags (symptoms absent). Positive symptom tags included ARAT-related AEs such as “pain,” “skin disorders,” “fatigue,” and “gastrointestinal symptoms.” Many other symptoms were classified as serious AEs. Furthermore, differences in symptom tag profiles reflecting pharmacists’ AE monitoring were observed between androgen synthesis inhibition and androgen receptor signaling inhibition. Conclusions: The NER system successfully extracted AEs from pharmaceutical care records of patients prescribed ARATs, demonstrating its potential to systematically track the presence and absence of AEs in outpatients. Based on the analysis of a large volume of pharmaceutical medical records using the NER system, community pharmacists not only detect potential AEs but also actively monitor the absence of severe AEs, offering valuable insights for the continuous improvement of patient safety management.

  • Evaluating the Efficacy of Premedication in Preventing Hypersensitivity Reactions to Nonionic Contrast Agents

    Suzuki S., Imai S., Omata A., Kamimura T., Kizaki H., Koinuma T., Hori S.

    Biological and Pharmaceutical Bulletin 48 ( 3 ) 241 - 245 2025.03

    ISSN  09186158

    　View Summary

    Iodine-based contrast agents can induce various acute hypersensitivity reactions ranging from mild itching or vomiting shortly after administration to severe hypotension or loss of consciousness. In Japan, steroid premedication is commonly used to prevent acute hypersensitivity reactions. However, little clear evidence supporting its efficacy is available. In this study, we evaluated the effectiveness of premedication for acute hypersensitivity reactions induced by nonionic iodine contrast agents using propensity score matching. The participants included patients who were administered nonionic iodine contrast agents at Yokohama City Minato Red Cross Hospital between April 1, 2016 and March 31, 2022. Only first-time patients with no history of hypersensitivity reactions to contrast agents were included. The patients were classified into premedication and non-premedication groups, and the incidence proportions of acute hypersensitivity reactions were compared after matching. Of the 19976 patients, 422 (211 in each group) were matched. In the premedication group, 7 cases (3.32%) of hypersensitivity reactions occurred. In contrast, only 2 cases (0.95%) were observed in the non-premedication group. The odds ratio for the occurrence of hypersensitivity reactions in the premedication group was 3.500 (95% confidence interval, 0.727–16.848), with no significant difference. Therefore, premedication did not demonstrate the efficacy in preventing acute hypersensitivity reactions induced by nonionic iodine contrast agents. Based on the results of this study and guidelines, a recommendation for premedication was not reached for patients with a history of allergies, including asthma and atopy, as well as those with a history of drug or food allergies.

  • 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-

    森部詩月，今井俊吾，佐山杏子，上村忠聖，林 誠一，木崎速人，堀 里子

    医薬品情報学（in press） ((一社)日本医薬品情報学会)  26 ( 4 ) 178 - 185 2025.02

    Research paper (scientific journal), Accepted,  ISSN  1345-1464

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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 】

  • 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)

  • 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)

  • 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)

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

  • 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.

  • 日本医療薬学会 第6回 フレッシャーズ・カンファランス優秀演題発表賞

    長谷川樹、矢田竣太郎、今井俊吾、木崎速人、荒牧英治、堀　里子, 2023.06,  システマティックレビュー更新時の自動文献精査モデル構築と論文要素の影響度評価

    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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