木崎 速人 (キザキ ハヤト)

Kizaki, Hayato

写真a

所属(所属キャンパス)

薬学部 薬学科 医薬品情報学講座 (芝共立)

職名

助教

経歴 【 表示 / 非表示

  • 2018年11月
    -
    継続中

    慶應義塾大学薬学部, 医薬品情報学講座, 助教

学歴 【 表示 / 非表示

  • 2010年04月
    -
    2014年03月

    東京大学, 薬学部, 薬科学科

    大学, 卒業

  • 2014年04月
    -
    2016年03月

    東京大学, 薬学系研究科, 薬科学専攻

    大学院, 修了, 修士

  • 2016年09月

    東京大学, 薬学系研究科, 薬科学専攻

    大学院, 博士

  • 2017年09月

    東京大学, 薬学系研究科, 薬学専攻

    大学院, 博士

学位 【 表示 / 非表示

  • 薬科学(修士), 東京大学, 課程, 2016年03月

    黄色ブドウ球菌コロニースプレッディングにおけるPSM毒素の役割

免許・資格 【 表示 / 非表示

  • 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年03月

  • 薬剤師免許, 2019年

 

研究分野 【 表示 / 非表示

  • ライフサイエンス / 医療薬学

  • ライフサイエンス / 医療管理学、医療系社会学

研究キーワード 【 表示 / 非表示

  • 介護施設

  • 医療安全

  • 医薬品情報

  • 多職種連携

  • 薬剤師

 

論文 【 表示 / 非表示

  • A cross-sectional survey of hepatitis B virus screening in patients who received immunosuppressive therapy for rheumatoid arthritis in Japan

    Yanagisawa Y., Imai S., Kizaki H., Hori S.

    Journal of Pharmaceutical Health Care and Sciences (Journal of Pharmaceutical Health Care and Sciences)  10 ( 1 ) 18 2024年12月

    ISSN  2055-0294

     概要を見る

    Background: Patients with a history of hepatitis B virus (HBV) infection who are receiving immunosuppressive therapy are at risk of HBV reactivation and disease. Therefore, HBV screening is required prior to administering antirheumatic drugs with immunosuppressive effects. This study aimed to determine the status of hepatitis B surface antigen (HBsAg), hepatitis B core antibody (HBcAb), and hepatitis B surface antibody (HBsAb) screening prior to the initiation of drug therapy, including new antirheumatic drugs, in patients with rheumatoid arthritis. Methods: This retrospective cross-sectional study used data from April 2014 to August 2022 from the Japanese hospital-based administrative claims database. The inclusion criteria were rheumatoid arthritis and first prescription date of antirheumatic drugs. Results: A total of 82,282 patients with rheumatoid arthritis who were first prescribed antirheumatic drugs between April 2016 and August 2022 were included. Of the eligible patients, 9.7% (n=7,959) were screened for all HBV (HBsAg, HBsAb, and HbcAb) within 12 months prior to the date of initial prescription. The HBsAg test was performed in 30.0% (n=24,700), HBsAb test in 11.8% (n=9,717), and HBcAb test in 13.1% (n=10,824) of patients. The proportion of patients screened for HBV infection has been increasing since 2018; however, the proportion of patients screened for rheumatoid arthritis remains low. Conclusions: Our findings suggest that HBV screening may be insufficient in patients who received antirheumatic drugs. With the increasing use of new immunosuppressive antirheumatic drugs, including biological agents, healthcare providers should understand the risk of HBV reactivation and conduct appropriate screening.

  • Concomitant use of lansoprazole and ceftriaxone is associated with an increased risk of ventricular arrhythmias and cardiac arrest in a large Japanese hospital database.

    Mitsuboshi S, Imai S, Kizaki H, Hori S

    The Journal of infection 89 ( 2 ) 106202 2024年06月

    ISSN  0163-4453

  • Medication incidents associated with the provision of medication assistance by non-medical care staff in residential care facilities.

    Kizaki H, Yamamoto D, Maki H, Masuko K, Konishi Y, Satoh H, Hori S, Sawada Y

    Drug discoveries & therapeutics 18 ( 1 ) 54 - 59 2024年03月

    ISSN  1881-7831

  • Factor Analysis of Patients Who Find Tablets or Capsules Difficult to Swallow Due to Their Large Size: Using the Personal Health Record Infrastructure of Electronic Medication Notebooks

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

    Journal of Medical Internet Research (Journal of Medical Internet Research)  26 ( 1 ) e54645 2024年01月

    ISSN  1439-4456

     概要を見る

    Background: Understanding patient preference regarding taking tablet or capsule formulations plays a pivotal role in treatment efficacy and adherence. Therefore, these preferences should be taken into account when designing formulations and prescriptions. Objective: This study investigates the factors affecting patient preference in patients who have difficulties swallowing large tablets or capsules and aims to identify appropriate sizes for tablets and capsules. Methods: A robust data set was developed based on a questionnaire survey conducted from December 1, 2022, to December 7, 2022, using the harmo smartphone app operated by harmo Co, Ltd. The data set included patient input regarding their tablet and capsule preferences, personal health records (including dispensing history), and drug formulation information (available from package inserts). Based on the medication formulation information, 6 indices were set for each of the tablets or capsules that were considered difficult to swallow owing to their large size and concomitant tablets or capsules (used as controls). Receiver operating characteristic (ROC) analysis was used to evaluate the performance of each index. The index demonstrating the highest area under the curve of the ROC was selected as the best index to determine the tablet or capsule size that leads to swallowing difficulties. From the generated ROCs, the point with the highest discriminative performance that maximized the Youden index was identified, and the optimal threshold for each index was calculated. Multivariate logistic regression analysis was performed to identify the risk factors contributing to difficulty in swallowing oversized tablets or capsules. Additionally, decision tree analysis was performed to estimate the combined risk from several factors, using risk factors that were significant in the multivariate logistic regression analysis. Results: This study analyzed 147 large tablets or capsules and 624 control tablets or capsules. The “long diameter + short diameter + thickness” index (with a 21.5 mm threshold) was identified as the best indicator for causing swallowing difficulties in patients. The multivariate logistic regression analysis (including 132 patients with swallowing difficulties and 1283 patients without) results identified the following contributory risk factors: aged <50 years (odds ratio [OR] 1.59, 95% CI 1.03-2.44), female (OR 2.54, 95% CI 1.70-3.78), dysphagia (OR 3.54, 95% CI 2.22-5.65), and taking large tablets or capsules (OR 9.74, 95% CI 5.19-18.29). The decision tree analysis results suggested an elevated risk of swallowing difficulties for patients with taking large tablets or capsules. Conclusions: This study identified the most appropriate index and threshold for indicating that a given tablet or capsule size will cause swallowing difficulties, as well as the contributory risk factors. Although some sampling biases (eg, only including smartphone users) may exist, our results can guide the design of patient-friendly formulations and prescriptions, promoting better medication adherence.

  • Adverse Event Signal Detection Using Patients’ Concerns in Pharmaceutical Care Records: Evaluation of Deep Learning Models

    Nishioka S., Satoshi W., Yanagisawa Y., Sayama K., Kizaki H., Imai S., Someya M., Taniguchi R., Yada S., Aramaki E., Hori S.

    Journal of Medical Internet Research (Journal of Medical Internet Research)  26 ( 1 ) e55794 2024年

    ISSN  1439-4456

     概要を見る

    Background: Early detection of adverse events and their management are crucial to improving anticancer treatment outcomes, and listening to patients’ subjective opinions (patients’ voices) can make a major contribution to improving safety management. Recent progress in deep learning technologies has enabled various new approaches for the evaluation of safety-related events based on patient-generated text data, but few studies have focused on the improvement of real-time safety monitoring for individual patients. In addition, no study has yet been performed to validate deep learning models for screening patients’ narratives for clinically important adverse event signals that require medical intervention. In our previous work, novel deep learning models have been developed to detect adverse event signals for hand-foot syndrome or adverse events limiting patients’ daily lives from the authored narratives of patients with cancer, aiming ultimately to use them as safety monitoring support tools for individual patients. Objective: This study was designed to evaluate whether our deep learning models can screen clinically important adverse event signals that require intervention by health care professionals. The applicability of our deep learning models to data on patients’ concerns at pharmacies was also assessed. Methods: Pharmaceutical care records at community pharmacies were used for the evaluation of our deep learning models. The records followed the SOAP format, consisting of subjective (S), objective (O), assessment (A), and plan (P) columns. Because of the unique combination of patients’ concerns in the S column and the professional records of the pharmacists, this was considered a suitable data for the present purpose. Our deep learning models were applied to the S records of patients with cancer, and the extracted adverse event signals were assessed in relation to medical actions and prescribed drugs. Results: From 30,784 S records of 2479 patients with at least 1 prescription of anticancer drugs, our deep learning models extracted true adverse event signals with more than 80% accuracy for both hand-foot syndrome (n=152, 91%) and adverse events limiting patients’ daily lives (n=157, 80.1%). The deep learning models were also able to screen adverse event signals that require medical intervention by health care providers. The extracted adverse event signals could reflect the side effects of anticancer drugs used by the patients based on analysis of prescribed anticancer drugs. “Pain or numbness” (n=57, 36.3%), “fever” (n=46, 29.3%), and “nausea” (n=40, 25.5%) were common symptoms out of the true adverse event signals identified by the model for adverse events limiting patients’ daily lives. Conclusions: Our deep learning models were able to screen clinically important adverse event signals that require intervention for symptoms. It was also confirmed that these deep learning models could be applied to patients’ subjective information recorded in pharmaceutical care records accumulated during pharmacists’ daily work.

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KOARA(リポジトリ)収録論文等 【 表示 / 非表示

総説・解説等 【 表示 / 非表示

  • 新薬まるわかり アルツハイマー型認知症治療薬 アリドネパッチ 27.5mg/55mg (ドネペジル)

    木崎速人,名倉慎吾,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)  315 2024年01月

    記事・総説・解説・論説等(商業誌、新聞、ウェブメディア), 共著

  • 新薬まるわかり 骨粗鬆症治療薬 オスタバロ皮下注カートリッジ 1.5mg (アバロパラチド酢酸塩)

    木崎速人,名倉慎吾,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)  313 2023年11月

    記事・総説・解説・論説等(商業誌、新聞、ウェブメディア), 共著

  • 新薬まるわかり 抗リウマチ薬 メトジェクト皮下注 7.5mgシリンジ0.15mL/10mgシリンジ0.20mL/12.5mgシリンジ0.25mL/15mgシリンジ0.30mL (メトトレキサート)

    木崎速人,中田雄大,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)  311 2023年11月

    記事・総説・解説・論説等(商業誌、新聞、ウェブメディア), 共著

  • 腎細胞がん患者に対するチロシンキナーゼ阻害薬誘発性高血圧の発現に関する実態調査及び関連因子の検討

    中西 慧, 池上 慶祐, 今井 俊吾, 木崎 速人, 堀 里子

    医療情報学連合大会論文集 ((一社)日本医療情報学会)  43回   693 - 697 2023年11月

    ISSN  1347-8508

  • 管理栄養士による地域薬局利用者の栄養サポートに向けた,住民の食生活の実態調査

    岡田 千乃, 中司 和希, 木崎 速人, 山田 真里, 安藤 湖乃, 衣笠 百合香, 徳村 百香, 加藤 芹奈, 山田 あすか, 中田 芽久美, 池田 裕樹, 宮本 興治, 今井 俊吾, 堀 里子

    New Diet Therapy ((一社)日本臨床栄養協会)  39 ( 2 ) 208 - 208 2023年09月

    ISSN  0910-7258

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研究発表 【 表示 / 非表示

  • 腎細胞がん患者に対するチロシンキナーゼ阻害薬誘発性高血圧 の発現に関する実態調査及び関連因子の検討

    中西慧,池上慶祐,今井俊吾, 木﨑速人,堀里子.

    第43回医療情報学連合大会, 

    2023年11月

    ポスター発表

  • 正則化ロジスティック回帰/LightGBMを用いた服薬状況に対する服薬アドヒアランス素因の関連性解析及び重要度の検討

    飯野温,木﨑速人,今井俊吾,堀里子.

    2023年11月

    口頭発表(一般)

  • External Validation and Update of the Risk Prediction Model of Denosumab-Induced Hypocalcemia Developed from Medical Big Data for Clinical Use.

    Keisuke Ikegami, Shungo Imai, Osamu Yasumuro, Masami Tsuchiya, Naomi Henmi, Mariko Suzuki, Katsuhisa Hayashi, Chisato Miura, Haruna Abe, Hayato Kizaki, Ryohkan Funakoshi, Yasunori Sato, Satoko Hori.

    FAPA 2023 (台湾) , 

    2023年10月

    ポスター発表

  • 自然言語処理モデルBERTを用いた医薬品関連インシデント要因抽出のためのマルチラベル分類器の構築

    江原沙也加,木﨑速人,渡部哲,今井俊吾,矢田竣太郎,荒牧英治,安室修,舟越亮寛,堀里子.

    2023年09月

    ポスター発表

  • 電子お薬手帳を基盤としたPHR活用による,「薬を大きくて飲みづらいと感じる」患者の予測モデル構築

    淺野 真輝,今井 俊吾,清水 友梨,木﨑 速人,吉田 奈央,島田 昌典,山東 崇紀,石島 知,堀 里子.

    医療薬学フォーラム2023/第31回クリニカルファーマシーシンポジウム, 

    2023年07月

    ポスター発表

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競争的研究費の研究課題 【 表示 / 非表示

  • 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年04月
    -
    2020年03月

    日本医薬品情報学会, 課題研究班, 木﨑速人、佐藤宏樹,堀里子,澤田康文, 研究代表者

受賞 【 表示 / 非表示

  • 第21介医薬品情報学会・学術大会 学生優秀発表ポスター賞

    木崎速人,佐藤宏樹,山本大輔,馬来秀行,益子幸太郎,小西ゆかり,浅井康平,堀里子,澤田康文, 2018年06月, 医薬品情報学会, 介護士による服薬介助に伴い発生したインシデントに関する記述疫学的解析

    受賞区分: 国内学会・会議・シンポジウム等の賞

 

担当授業科目 【 表示 / 非表示

  • 卒業研究1(薬学科)

    2024年度

  • 実務実習事前学習(実習)

    2024年度

  • 英語演習(薬学科)

    2024年度

  • デジタルヘルスと未来の医療

    2024年度

  • EBMの実践

    2024年度

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担当経験のある授業科目 【 表示 / 非表示

  • 実務実習事前学習(実習)

    慶應義塾

    2018年04月
    -
    2019年03月

    秋学期, 実習・実験, 160人

教育活動及び特記事項 【 表示 / 非表示

  • 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年11月

    , その他特記事項

  • 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年05月

    , その他特記事項

  • 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年04月

    , その他特記事項

  • 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年03月

    , その他特記事項

  • 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年03月

    , その他特記事項

 

所属学協会 【 表示 / 非表示

  • 日本薬学会, 

    2020年
    -
    継続中
  • 医薬品情報学会

     
  • 医療薬学会

     

委員歴 【 表示 / 非表示

  • 2020年04月
    -
    継続中

    研究企画委員会 委員, 一般財団法人 日本医薬品情報学会