Yanagisawa, Yuki

写真a

Affiliation

Faculty of Pharmacy, Department of Pharmacy 医薬品情報学講座 (Shiba-Kyoritsu)

Position

Project Assistant Professor (Non-tenured)/Project Research Associate (Non-tenured)/Project Instructor (Non-tenured)
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Career 【 Display / hide 】

  • 2016.04
    -
    2022.03

    横浜労災病院, 薬剤部, 薬剤師

  • 2022.04
    -
    2023.03

    Teikyo Heisei University, 薬学部薬学科, 助教

  • 2022.04
    -
    2023.12

    Tokyo Metropolitan Children's Medical Center, Department of pharmacy, Pharmacist

  • 2023.04
    -
    2024.03

    Keio University, Faculty of Pharmacy, Division of drug Informatics, Researcher

  • 2024.01
    -
    Present

    Tokyo Metropolitan Bokutoh Hospital, Department of pharmacy, Pharmacist

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Academic Background 【 Display / hide 】

  • 2010.04
    -
    2016.03

    Tokyo University of Pharmacy and Life Science, School of Pharmacy, School of Pharmacy

  • 2018.04
    -
    2022.03

    Teikyo Heisei University, Graduate School of Pharmaceutical Science, 博士課程

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Academic Degrees 【 Display / hide 】

  • 博士（薬学）, Teikyo Heisei University, Coursework, 2022.03

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Licenses and Qualifications 【 Display / hide 】

  • 薬剤師, 2016

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Research Areas 【 Display / hide 】

  • Life Science / Clinical pharmacy

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Research Keywords 【 Display / hide 】

  • Drug Informatics

  • clinical pharmacy

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Papers 【 Display / hide 】

  • 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 )  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.

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

    Tatsuki Hasegawa, Hayato Kizaki, Keisho Ikegami, Shungo Imai, Yuki Yanagisawa, Shuntaro Yada, Eiji Aramaki, Satoko Hori

    JMIR Medical Informatics 13 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

    Yuki Yanagisawa, Satoshi Watabe, Sakura Yokoyama, Kyoko Sayama, Hayato Kizaki, Masami Tsuchiya, Shungo Imai, Mitsuhiro Someya, Ryoo Taniguchi, Shuntaro Yada, Eiji Aramaki, Satoko Hori

    JMIR Cancer (JMIR Publications Inc.)  11   e69663 - e69663 2025.03

    Lead author

    　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.

  • Nationwide Survey on the Current Status and Issues in Analyzing Drug-related Incident Reports in Hospitals

    Sayaka Ebara, Yuki Yanagisawa, Hayato Kizaki, Shungo Imai, Osamu Yasumuro, Ryohkan Funakoshi, Satoko Hori

    Japanese Journal of Pharmaceutical Health Care and Sciences (Japanese Society of Pharmaceutical Health Care and Sciences)  51 ( 1 ) 10 - 19 2025.01

    Accepted,  ISSN  1346-342X

  • Analysis of Overdose-related Posts on Social Media

    Ryuya Sato, Masami Tsuchiya, Rintaro Ichiyama, Soma Hisamura, Satoshi Watabe, Yuki Yanagisawa, Tomohiro Nishiyama, Shuntaro Yada, Eiji Aramaki, Hayato Kizaki, Shungo Imai, Satoko Hori

    YAKUGAKU ZASSHI (Pharmaceutical Society of Japan)  144 ( 12 ) 1125 - 1135 2024.12

    ISSN  0031-6903

    　View Summary

    Intentional overdose (OD) of over-the-counter (OTC) and prescription drugs is becoming a significant social issue all over the world. While previous research has focused on drug misuse, there has been limited analysis using social networking service data. This study aims to analyze posts related to a drug overdose on Twitter<sup>®</sup> (X<sup>®</sup>) to understand the characteristics and trends of drug misuse, and to examine the applicability of social media in understanding the current situation of OD through natural language processing techniques. We collected posts in Japanese containing the term“OD”from January 10 to February 8, 2023, and analyzed 30203 posts. Using a pre-trained, fine-tuned bidirectional encoder representations from transformers (BERT) model, we classified the posts into categories, including direct mentions of OD. We examined the content for drug types and emotional context. Among the 5283 posts categorized as“Posts describing ODing,”about one-third included specific drug names or related terms. The most frequently mentioned OTC drugs included active ingredients such as codeine, dextromethorphan, ephedrine, and diphenhydramine. Prescription drugs, particularly benzodiazepines and pregabalin, were also common. Tweets peaked at midnight, suggesting a link between negative emotions and potential OD incidents. Our classifier showed high accuracy in distinguishing OD-related posts. Analyzing Twitter<sup>®</sup> posts provides valuable insights into the patterns and emotional contexts of drug misuse. Monitoring social networking services for OD-related content could help identify high-risk individuals and inform prevention strategies. Enhanced monitoring and public awareness are crucial to reducing the risks associated with both OTC and prescription drug misuse.

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