Development of High-Precision Models for Predicting Disease Onset, Elucidating Pathogenesis, and Forecasting Treatment Response in Refractory Disease
The Big Data Omics Research Team integrates various types of real-world data (RWD), such as electronic medical records, genomic data, pathological images and laboratory values, in order to develop highly precise models that can predict disease onset and elucidate pathophysiology. These models can also predict how patients will respond to treatment for intractable diseases.
Dr Kitai (Respiratory Medicine) uses large language models (LLMs) to organise clinical information and generate analysable datasets. He is developing diagnostic support models for predicting prognosis and assessing severe disease risk, with the aim of applying these to personalised medicine. Dr Hatanaka (Pathology) uses AI-based pathomics and RNA sequencing analysis on pathology images obtained from a digital pathology database to enable the quantitative characterisation of tumour microenvironments and other factors.
