Shuntaro Yada

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Assistant Professor

Nara Institute of Science and Technology（NAIST）

Japan

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About

Shuntaro Yada is an Assistant Professor at the Nara Institute of Science and Technology (NAIST) in Japan. His research focuses on the application of natural language processing in healthcare, with recent articles examining topics such as the preventive effects of medications, systematic review updates, and the extraction of clinical data from electronic health records. He is also involved in studies assessing the performance of large language models in medical contexts, particularly in relation to genetic counseling and cancer care.

Recent Grants

Grant: Close

デジタル環境における偶然の情報遭遇を促すユーザインタフェースの設計と検証

Open Date: 2020-04-01

Close Date:

Grant: Close

オンラインを介して「前読書家」の読書を触発する方式・環境の開発

Open Date: 2016-04-01

Close Date: 2019-03-01

Grant: Close

学校図書館による教員サポートを促す図書推薦システムの開発と評価

Open Date: 2014-06-01

Close Date: 2015-03-01

Articles (21)

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2020

Collaborators (7)

Tomohiro Nishiyama

Nara Institute of Science and Technology

JAPAN

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Hayato Kizaki

Assistant Professor

Keio University

JAPAN

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Shoko Wakamiya

Associate Professor

Nara Institute of Science and Technology（NAIST）

JAPAN

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Eiji ARAMAKI

Professor

Nara Institute of Science and Technology（NAIST）

JAPAN

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Kongmeng Liew

Lecturer

Nara Institute of Science and Technology

NEW ZEALAND

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Yoshimasa Kawazoe

The Univeristy of Tokyo

JAPAN

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Satoko Hori

Keio University

JAPAN

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Shuntaro Yada

Research Interests

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About

Recent Grants

Articles (21)

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

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

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

Impact of Attached File Formats on the Performance of ChatGPT-4 on the Japanese National Nursing Examination: Evaluation Study

Evaluating and Enhancing Japanese Large Language Models for Genetic Counseling Support: Comparative Study of Domain Adaptation and the Development of an Expert-Evaluated Dataset

Assessing domain adaptation in adverse drug event extraction on real-world breast cancer records

Utility analysis and demonstration of real-world clinical texts: A case study on Japanese cancer-related EHRs

Exploring a method for extracting concerns of multiple breast cancer patients in the domain of patient narratives using BERT and its optimization by domain adaptation using masked language modeling

Predictive Model for Extended-Spectrum β-Lactamase–Producing Bacterial Infections Using Natural Language Processing Technique and Open Data in Intensive Care Unit Environment: Retrospective Observational Study

Is Boundary Annotation Necessary? Evaluating Boundary-Free Approaches to Improve Clinical Named Entity Annotation Efficiency: Case Study

Differing Content and Language Based on Poster-Patient Relationships on the Chinese Social Media Platform Weibo: Text Classification, Sentiment Analysis, and Topic Modeling of Posts on Breast Cancer

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

Automated System to Capture Patient Symptoms from Multi-type Japanese Clinical Texts: Natural Language Processing Approach (Preprint)

Post-marketing surveillance of anticancer drugs using natural language processing of electronic medical records

Transferability Based on Drug Structure Similarity in the Automatic Classification of Noncompliant Drug Use on Social Media: Natural Language Processing Approach

Extracting Multiple Worries From Breast Cancer Patient Blogs Using Multilabel Classification With the Natural Language Processing Model Bidirectional Encoder Representations From Transformers: Infodemiology Study of Blogs

Estimation of Psychological Distress in Japanese Youth Through Narrative Writing: Text-Based Stylometric and Sentiment Analyses

Measuring Public Concern About COVID-19 in Japanese Internet Users Through Search Queries: Infodemiological Study

Medical Needs Extraction for Breast Cancer Patients from Question and Answer Services: Natural Language Processing-Based Approach

Identification of tweets that mention books

Identification of Adverse Drug Event–Related Japanese Articles: Natural Language Processing Analysis

Collaborators (7)

Tomohiro Nishiyama

Hayato Kizaki

Shoko Wakamiya

Eiji ARAMAKI

Kongmeng Liew

Yoshimasa Kawazoe

Satoko Hori

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