KEIO RESEARCHERS INFORMATION SYSTEM

Career 【 Display / hide 】

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

Academic Background 【 Display / hide 】

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

Academic Degrees 【 Display / hide 】

Academic Degrees 【 Display / hide 】

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

Licenses and Qualifications 【 Display / hide 】

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

Research Areas 【 Display / hide 】

Research Areas 【 Display / hide 】

• Life Science / Clinical pharmacy

• Life Science / Medical management and medical sociology

Research Keywords 【 Display / hide 】

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

Papers 【 Display / hide 】

Papers 【 Display / hide 】

• 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^®, 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.

  • Access to Document (DOI)

• 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.

  • Access to Document (DOI)

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

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

  Studies in health technology and informatics 329   703 - 707 2025.08

  ISSN  0926-9630

  　View Summary

  This study assessed the effectiveness of natural language processing (NLP) in detecting adverse events (AEs) from anticancer agents by analyzing data from over 39,000 cancer patients. A specialized machine learning model identified known AEs from anticancer agents like capecitabine, oxaliplatin, and anthracyclines, revealing a significantly higher incidence in the treatment groups compared to non-users. While the NLP approach effectively detected most symptomatic AEs requiring manual review, it struggled with rarely documented conditions and commonly used clinical terms. Overall, the method shows promise for automated AE detection in medical records, particularly for symptoms without laboratory markers or diagnosis codes.

  • Access to Document (DOI)

• 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

  Studies in health technology and informatics 329   758 - 763 2025.08

  ISSN  0926-9630

  　View Summary

  Analyzing medication-related incident reports is crucial for patient safety; however, systematically extracting the underlying factors contributing to incident occurrence remains challenging. We developed a multi-label classifier that automatically identified incident factors from 1,212 drug-related incident reports using the Bidirectional Encoder Representations from Transformers and its derivatives. Based on the P-mSHELL model, a comprehensive framework for incident factor analysis, we established seven distinct factor categories and evaluated various pre-trained models through five-fold cross-validation. Almost all models achieved macro F1 scores exceeding 0.6, with the lightweight A Lite BERT model showing comparable performance to BERT. This study demonstrates the practical feasibility of natural language processing techniques for systematic incident factor analysis, supporting enhanced patient safety management.

  • Access to Document (DOI)

• Elucidating Celecoxib's Preventive Effect in Capecitabine-Induced Hand-Foot Syndrome Using Medical Natural Language Processing.

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

  JCO clinical cancer informatics 9   e2500096 2025.08

  　View Summary

  PURPOSECapecitabine, an oral anticancer agent, frequently causes hand-foot syndrome (HFS), affecting patients' quality of life and treatment adherence. However, such symptomatic toxicities are often difficult to detect in structured electronic health record (EHR) data. This study primarily aimed to validate a natural language processing (NLP) approach to identifying capecitabine-induced HFS from unstructured clinical text and demonstrate its application in evaluating medication-associated adverse event trends in real-world settings.METHODSWe conducted a retrospective cohort study using EHRs from the University of Tokyo Hospital (2004-2021). HFS cases were identified using the MedNERN-CR-JA NLP model. After propensity score matching, we compared capecitabine users with and without celecoxib and assessed time to HFS onset using Cox proportional hazards models. NLP-based HFS detection was validated through manual annotation of aggregated clinical notes. Negative control and sensitivity analyses ensured robustness.RESULTSAmong 44,502 patients with cancer, 669 capecitabine users were analyzed. HFS incidence was significantly higher among capecitabine users (hazard ratio [HR], 1.93 [95% CI, 1.48 to 2.52]; P <.001) compared with nonusers. Celecoxib use showed a suggestive association with a reduced HFS risk (HR, 0.51 [95% CI, 0.24 to 1.07]; P =.073). The NLP model demonstrated high accuracy in identifying HFS, achieving a precision of 0.875, recall of 1.000, and F<inf>1</inf> score of 0.933, based on manual annotation of patient-level clinical notes. Outcome trends remained consistent when using manually annotated HFS case labels instead of NLP-detected events, supporting the method's robustness.CONCLUSIONThese findings demonstrate the effectiveness of NLP in detecting HFS from real-world clinical records. The application to celecoxib-HFS detection illustrates the potential utility of this approach for retrospective safety analysis. Further work is needed to evaluate generalizability across diverse clinical settings.

  • Access to Document (DOI)

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

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

• Development of a fall risk assessment tool for the prevention of falls in nursing homes in Japan.

  Kizaki, Hayato

  学事振興資金研究成果実績報告書 (慶應義塾大学)  2020

Reviews, Commentaries, etc. 【 Display / hide 】

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 】

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.

  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.

  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

  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

  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

  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 】

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

• 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

Awards 【 Display / hide 】

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 】

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 】

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

Educational Activities and Special Notes 【 Display / hide 】

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

Memberships in Academic Societies 【 Display / hide 】

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

  　

Committee Experiences 【 Display / hide 】

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