Yahagi, Naohisa

写真a

Affiliation

Graduate School of Media and Governance (Shonan Fujisawa)

Position

Professor

External Links
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Career 【 Display / hide 】

  • 2004.04
    -
    2007.09

    横浜市立市民病院, 小児科, 医務職員

  • 2007.10
    -
    2011.09

    国立成育医療研究センター, 治験管理室, 臨床研究フェロー

  • 2011.10
    -
    2013.03

    国立成育医療研究センター, 治験ネットワーク推進室, 専門職

  • 2013.04
    -
    2015.03

    National Center for Child Health and Development, Clinical Research Network Management Office, Assistant Director

  • 2015.04
    -
    2016.09

    National Center for Child Health and Development, Division for Data Science and System Strategy, Deputy Manager

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Academic Background 【 Display / hide 】

  • 1994.04
    -
    2000.03

    Keio University, 医学部

    University, Graduated, Master's course

  • 2000.04
    -
    2004.03

    Keio University, 医学研究科

    Graduate School, Completed, Doctoral course

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Academic Degrees 【 Display / hide 】

  • 学士（医学）, Keio University, Coursework, 2000.03

  • 博士号(医学), Keio University, Coursework, 2004.03

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Licenses and Qualifications 【 Display / hide 】

  • 医師免許証, 2000.04

  • 小児科専門医, 2008.05

  • 認定小児科指導医, 2016.12

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Research Areas 【 Display / hide 】

  • Humanities & Social Sciences / Business administration (Strategy)

  • Social Infrastructure (Civil Engineering, Architecture, Disaster Prevention) / Social systems engineering (ヘルスケアシステムデザイン)

  • Life Science / Embryonic medicine and pediatrics

  • Life Science / Medical systems (clinical informatics)

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Research Keywords 【 Display / hide 】

  • Strategy for value-based healthcare delivery

  • Clinical Informatics

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Books 【 Display / hide 】

  • 2050年の入試問題

    神成淳司,田中浩也,脇田　玲,矢作尚久,安宅和人,池澤　あやか,石川将也,大山エンリコイサム,たかまつなな,清水唯一朗,一青窈,本城慎之介, 日本経済新聞出版, 2022.03

  • 競争優位に導く業務改善とイノベーション

    矢作尚久, 日本医療企画, 2022.02

  • 小児救急トリアージテキスト

    伊藤龍子，矢作尚久, 医歯薬出版, 2010.03

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Papers 【 Display / hide 】

  • Novel Approach for Detecting Respiratory Syncytial Virus in Pediatric Patients Using Machine Learning Models Based on Patient-Reported Symptoms: Model Development and Validation Study

    Shota Kawamoto, Yoshihiko Morikawa, Naohisa Yahagi

    JMIR FORMATIVE RESEARCH  2024.04

    Research paper (scientific journal), Last author, Corresponding author, Accepted

    　View Summary

    Background: Respiratory syncytial virus (RSV) affects children, causing serious infections, particularly in high-risk groups.
    Given the seasonality of RSV and the importance of rapid isolation of infected individuals, there is an urgent need for more efficient diagnostic methods to expedite this process.
    Objective: This study aimed to investigate the performance of a machine learning model that leverages the temporal diversity of symptom onset for detecting RSV infections and elucidate its discriminatory ability.
    Methods: The study was conducted in pediatric and emergency outpatient settings in Japan. We developed a detection model that remotely confirms RSV infection based on patient-reported symptom information obtained using a structured electronic template incorporating the differential points of skilled pediatricians. An extreme gradient boosting–based machine learning model was developed using the data of 4174 patients aged ≤24 months who underwent RSV rapid antigen testing. These patients visited either the pediatric or emergency department of Yokohama City Municipal Hospital between January 1, 2009, and December 31, 2015. The primary outcome was the diagnostic accuracy of the machine learning model for RSV infection, as determined by rapid antigen testing, measured using the area under the receiver operating characteristic curve. The clinical efficacy was evaluated by calculating the discriminative performance based on the number of days elapsed since the onset of the first symptom and exclusion rates based on thresholds of reasonable sensitivity and specificity.
    Results: Our model demonstrated an area under the receiver operating characteristic curve of 0.811 (95% CI 0.784-0.833) with good calibration and 0.746 (95% CI 0.694-0.794) for patients within 3 days of onset. It accurately captured the temporal evolution of symptoms; based on adjusted thresholds equivalent to those of a rapid antigen test, our model predicted that 6.9% (95% CI 5.4%-8.5%) of patients in the entire cohort would be positive and 68.7% (95% CI 65.4%-71.9%) would be negative. Our model could eliminate the need for additional testing in approximately three-quarters of all patients.
    Conclusions: Our model may facilitate the immediate detection of RSV infection in outpatient settings and, potentially, in home environments. This approach could streamline the diagnostic process, reduce discomfort caused by invasive tests in children, and allow rapid implementation of appropriate treatments and isolation at home. The findings underscore the potential of machine learning in augmenting clinical decision-making in the early detection of RSV infection.

  • Effectiveness of Clinical Management of COVID-19 Based on Structured Clinical Knowledge and Process Paths

    Tsuru S, Tamamoto T, Nakao A, Machida Y, Tanizaki K, Yahagi N.

    Studies in Health Technology and Informatics 310   359 - 363 2024.01

    Research paper (scientific journal), Last author, Accepted

  • The Impact of Cold Ambient Temperature in the Pattern of Influenza Virus Infection

    Eri M, Shota K, Yoshihiko M, Naohisa Y

    Open Forum Infectious Diseases (Oxford University Press)  10 ( 2 ) 1 - 6 2023.01

    Research paper (scientific journal), Joint Work, Last author, Corresponding author, Accepted

    　View Summary

    Abstract
    Background: Prior literature suggests that cold temperature strongly influences the immune function of animals and human behaviors, which may allow for the transmission of respiratory viral infections. However, information on the impact of cold stimuli, especially the impact of temporal change in the ambient temperature on influenza virus transmission, is limited.
    Methods: A susceptible-infected-recovered-susceptible model was applied to evaluate the effect of temperature change on influenza virus transmission.
    Results: The mean temperature of the prior week was positively associated with the number of newly diagnosed cases (0.107 [95% Bayesian credible interval {BCI}, .106-.109]), whereas the mean difference in the temperature of the prior week was negatively associated (-0.835 [95% BCI, -.840 to -.830]). The product of the mean temperature and mean difference in the temperature of the previous week were also negatively associated with the number of newly diagnosed cases (-0.192 [95% BCI, -.197 to -.187]).
    Conclusions: The mean temperature and the mean difference in temperature affected the number of newly diagnosed influenza cases differently. Our data suggest that high ambient temperature and a drop in the temperature and their interaction increase the risk of infection. Therefore, the highest risk of infection is attributable to a steep fall in temperature in a relatively warm environment.
    Keywords: SIRS model; cold stimuli; influenza.

  • Patient Data Sharing and Reduction of Overtime Work of Nurses by Innovation of Nursing Records Using Structured Clinical Knowledge

    Satoko Tsuru, Tetsuro Tamamoto, Akihiro Nakao, Kouichi Tanizaki, Naohisa Yahagi

    Studies in Health Technology and Informatics 294   525 - 529 2022.05

    Research paper (scientific journal), Joint Work, Last author, Accepted

  • 脳血管疾患後のリハビリテーションにおける標準的なリハビリ計画作成手法の開発

    加藤省吾，水流聡子，井手睦，進藤晃, 矢作尚久, 山田秀

    日本臨床知識学会誌 2   44 - 45 2021

    Research paper (scientific journal), Joint Work, Accepted

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

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Reviews, Commentaries, etc. 【 Display / hide 】

  • Healthcare as a social system

    NAOHISA YAHAGI

    化学工学 89 ( 1 ) 40 - 42 2025.01

    Article, review, commentary, editorial, etc. (scientific journal), Single Work, Lead author, Corresponding author

    　View Summary

    Modern medicine is undergoing a profound transformation driven by rapid advancements in cutting-edge technologies, including artificial intelligence (AI), advanced manufacturing, precision medicine, and precision healthcare. These innovations redefine medicine as a critical societal infrastructure, facilitating personalized care tailored to individual patient needs.

    At the heart of this transformation is the enhanced capability to accurately monitor patient conditions in real-time. This real-time tracking fundamentally reshapes healthcare delivery, improving efficiency and medical treatment quality. Notably, advanced technologies empower healthcare providers to administer timely and precise medical interventions, significantly optimizing clinical efficiency. For patients, this translates into reduced time spent in medical facilities, minimized healthcare costs, and improved overall patient experience.

    Furthermore, these technological developments pave the way for a society where healthcare seamlessly integrates into everyday life, allowing patients to manage their health effortlessly and unobtrusively. Sophisticated real-time monitoring technology enhances individual patient care and optimizes the distribution and utilization of medical resources. This systemic approach significantly reduces waste—such as excess inventory—and strengthens the healthcare system's financial sustainability.

    Each step, from pharmaceutical manufacturing to patient delivery, is meticulously engineered to maximize efficiency and eliminate redundancy. This comprehensive, systems-engineering perspective ensures the optimal deployment of medical resources—personnel, materials, and finances—positioning modern medicine as a robust, efficient, and integral societal foundation.

  • Artificial Intelligence in Clinical Decision Support: Challenges, Opportunities, and Future Perspectives

    Shota Kawamoto, Naohisa Yahagi

    Journal of Medical Technology (医学書院)  68 ( 12 ) 1453 - 1459 2024.12

    Article, review, commentary, editorial, etc. (scientific journal), Joint Work, Last author, Corresponding author

    　View Summary

    In recent years, the rapid advancement of artificial intelligence (AI) applications in healthcare, such as advanced diagnostic algorithms and automated systems, has significantly transformed medical practice. AI is increasingly anticipated to support healthcare professionals by streamlining various administrative tasks and assisting clinical decision-making. AI is effectively reducing the workload related to interpreting radiological and histopathological images, analyzing patient data, and automating routine administrative tasks. These advancements allow medical professionals to allocate more time to patient care, thus enhancing diagnostic accuracy, treatment precision, and overall operational efficiency.

    Beyond these direct clinical benefits, AI's potential extends significantly to optimizing healthcare systems. For instance, by integrating advanced predictive analytics, AI can foresee optimal intervention timings, efficiently manage resource allocation, and effectively support preventive healthcare strategies. If deployed as accessible applications beyond traditional healthcare settings, medical AI could facilitate early interventions and personalized preventive healthcare measures, proactively addressing health issues and reducing long-term healthcare costs.

    To fully realize this potential, medical support AI requires comprehensive societal infrastructure, which includes resolving critical technical challenges, addressing ethical considerations, developing a clear regulatory framework, and fostering specialized human resources. Issues such as the frame problem—where AI must discern relevant from irrelevant data—and the explicit integration of clinician tacit knowledge must be adequately addressed. AI applications in healthcare demonstrate considerable promise, particularly in diagnostic support domains such as infectious disease management through continuous symptom modeling and in enhancing treatment strategies through clinician experience-based insights.

    Ultimately, the transformative impact of medical support AI hinges on a collaborative societal approach. Establishing an ethically sound, legally robust, and technically competent environment will ensure that AI truly enhances the quality, accessibility, and efficiency of healthcare.

  • 診療支援システムの実際の運用（小児科）

    森田英明, 矢作尚久

    小児科診療 (診断と治療社)  85 ( 12 ) 1605 - 1612 2022.12

    Article, review, commentary, editorial, etc. (other), Last author, Corresponding author

  • 診療支援システムの全体構想

    森川和彦, 矢作尚久

    小児科診療 (診断と治療社)  85 ( 12 ) 1595 - 1603 2022.12

    Article, review, commentary, editorial, etc. (other), Joint Work, Last author, Corresponding author

  • 小児医療における情報収集システムの整備

    加藤省吾, 矢作尚久

    小児科診療 (診断と治療社)  85 ( 12 ) 1583 - 1587 2022.12

    Article, review, commentary, editorial, etc. (other), Joint Work, Last author, Corresponding author

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Presentations 【 Display / hide 】

  • Reducing Invasive Diagnostic Testing in Pediatrics Through Machine Learning: A Case Study Using RSV Testing

    Shota Kawamoto, Yoshihiko Morikawa, M.D., Naohisa Yahagi, M.D., Ph.D.

    Pediatric Academic Societies) Meeting 2025 (PAS2025), 

    2025.04
    , 

    Oral presentation (general)

  • Real-Time Patient State-Based Optimization Strategies for High-Cost Drug Distribution

    Shota Kawamoto, Yoshihiko Morikawa, M.D., Hirokazu Sugiyama, Ph.D., Naohisa Yahagi, M.D., Ph.D.

    AMIA 2024 Annual Symposium, 

    2024.11
    , 

    Poster presentation

  • The Present and Future of Digital Health

    Naohisa Yahagi, M.D., Ph.D.

    ISPE日本本部年次大会, 

    2023.05
    , 

    Symposium, workshop panel (nominated)

  • The Potential in Structured Process for Dysphagia Rehabilitation after Cerebrovascular Diseases

    Shogo Kato, Satoko Tsuru, Makoto Ide, Akira Shindo, Naohisa Yahagi, Shu Yamada

    Proc. of the AMIA 2022 Informatics Summit, 

    2022.03
    , 

    Oral presentation (general)

  • 寒冷刺激が人体の免疫応答に与える影響を組み込んだ ウィルス感染症の流行シミュレーションモデルの構築

    川本章太, 加藤省吾, 宮本佳明, 森川 和彦, 矢作 尚久

    化学工学会第52回秋季大会要旨集, VE123, 

    2021.09
    , 

    Oral presentation (general)

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

  • Development of medical equipment for medical support system for newborns to improve neonatal mortality in developing and emerging countries

    2024.08
    -
    2027.03

    Research grant, Coinvestigator(s)

  • 医療機関・在宅・介護施設を横断する脳血管疾患リハビリの標準計画作成手法の開発

    2022.04
    -
    2027.03

    日本学術振興会, Research grant, Coinvestigator(s)

  • 新生児診療支援システムを用いた診療・特定認定看護師の臨床推論力の向上に関する研究

    2022.04
    -
    2025.03

    Research grant, Coinvestigator(s)

  • パンデミック治療薬の迅速生産支援システムの構築

    2021
    -
    2024

    日本学術振興会, 科学研究費助成事業, Research grant, Coinvestigator(s)

  • 医療の質に資する分析を可能とするデータの質・構造の評価研究

    2021

    一般財団法人医療保険業務研究協会, Commissioned ｒesearch, Other

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Awards 【 Display / hide 】

  • 経済産業省令和3年度国際標準化貢献者表彰（産業技術環境局長表彰）

    2021

    Type of Award： Other

  • SFC Faculty Award(教育分野)

    2020

    Type of Award： Keio commendation etc.

  • 国際新生児学会優秀演題賞

    2009

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

  • 日本救急小児科学会優秀賞

    2005

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

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Courses Taught 【 Display / hide 】

  • WORKSHOPS ON FUTURE VISION

    2025

  • STRATEGIC MANAGEMENT

    2025

  • SEMINAR B

    2025

  • REDEFINING HEALTHCARE SYSTEM

    2025

  • PHYSICAL AND MENTAL HEALTH FOR CAMPUS LIFE

    2025

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Academic Activities 【 Display / hide 】

  • 情報処理学会誌

    2020
    -
    Present

  • American Medical Informatics Association

    2018
    -
    Present

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Committee Experiences 【 Display / hide 】

  • 2020
    -
    Present

    情報処理学会誌 Reviewer

  • 2020
    -
    Present

    公益財団法人日本学校保健会　成長曲線普及推進委員

  • 2019
    -
    Present

    国立研究開発法人　日本医療研究開発機構　課題評価委員

  • 2019
    -
    2020

    内閣官房健康・医療戦略室標準的医療情報システムに関する検討会　構成員

  • 2018
    -
    Present

    American Medical Informatics Association　Reviewer

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