Eiryo KAWAKAMI Professor

Eiryo KAWAKAMI
Professor

Researchmap

• Chiba University Institute for Advanced Academic Research / Graduate School of Medicine

• Keywords

  Gene regulatory network, Transcription factor, MicroRNA, Atopic dermatitis, Gene set enrichment analysis, Multilayered network, Regulatory network, State transition model, Time series analysis, Machine learning, Bifurcation, State space model, Disease prediction, Biomarker

• Professional Memberships

  Japanese Association for Medical Artificial Intelligence, Japanese Society for Bioinformatics, The Molecular Biology Society of Japan, Japanese Society for Immunology, The Japan Society of Human Genetics, Japanese hip society

Research Theme

Stratification and Prediction of Disease Onset/Progression for Preventive and Preemptive Medicine

Abstract

Recently, artificial intelligence (AI) and data science have been rapidly introduced into the medical and healthcare fields.

However, most of the cases were for disease detection and diagnosis based on data acquired at a specific time point, such as the first visit.

On the other hand, there is an increasing need for preventive and preemptive medical care for chronic diseases, such as lifestyle-related illnesses that have become a problem in modern society.

It is important for chronic diseases to understand and predict the long-term development of symptoms and prevent severe changes before they occur.

We are developing and implementing minimally- and non-invasive measurement methods, such as wearable devices and saliva measurements, and AI and mathematical methods for early detection and prediction of diseases.

We are also partnering with medical institutions, including Chiba University Hospital, to carry out research on stratification and prediction of diseases using large-scale clinical samples and state-of-the-art AI and data science technologies.