日時：2025年6月12日（木）
14時受付開始
14時30分開始～15時45分(最大16時)終了（逐次通訳を入れる予定です）
産官学連携プロジェクト

参加費は以下の通りです．
　一般会員・賛助会員：3,000円
　非会員：5,000円
　学生会員：1,000円
　学生非会員：2,000円

Introduction to Real-World Data: AI-Driven Insights from a Case Study

Outline:
In this session, Introduction to Real-World Data: AI-Driven Insights from a Case Study, we will examine how artificial intelligence and machine learning are reshaping the use of real-world data (RWD) to drive meaningful insights. We will start with an overview of RWD, discussing its key sources, its importance in healthcare research, and the challenges it presents. We will then walk through a real-world case study that demonstrates the full process of applying AI to extract value from complex datasets. Highlights will include the use of Natural Language Processing (NLP) for data standardization and patient identification, as well as the application of machine learning models to predict the disease of interest and trace the patient journey leading to diagnosis. The session will showcase how AI-driven methods can improve data quality, accelerate the generation of insights, and enable better decision-making in clinical research and healthcare. We will close with key lessons learned, best practices, and considerations for applying these techniques in future RWD projects.

Bio Sketch – Ashwin Kumar Rai, BE, MS

Ashwin Kumar Rai, MSc, is the Director of Data Science & Advanced Analytics in the Real-World Evidence (RWE) group at Evidera, a PPD business under Thermo Fisher Scientific. Working remotely from Kansas, he has over 16 years of experience in data science, real-world data (RWD) analytics, and machine learning (ML), with a focus on using advanced technologies to support healthcare research and improve patient outcomes.

Mr. Rai is a recognized thought leader in the field (real-world data) and has delivered invited presentations at major industry forums, including PHUSE, ISPOR, the FDA Innovation Challenge, Value-Added Medicine Week, the ACRO AI Workshop, and international events such as Japan’s SCDM and the Kagoshima Data Science Symposium. His work has been published in peer-reviewed journals such as Value in Health, ESC Heart Failure, and BMC Medical Research Methodology. In addition to his role at Evidera, he actively contributes to the advancement of real-world evidence by serving as Co-Chair of the PHUSE Real-World Evidence (RWE) Working Group and as ISPOR’s SIG Member Engagement Co-Chair. His commitment to innovation is reflected in numerous publications, abstracts, and poster presentations focused on AI and ML applications in healthcare. Notably, his AI prototypes were selected for presentation at the PHUSE/FDA Data Science Innovation Challenge in both 2020 and 2024, and his paper on predicting screen failure rates in human abuse potential studies was selected for presentation at the 30th ECNP Conference in Paris.