人體生物資料智慧醫療計畫 Smart Health Project

“Smart Health” is an emerging field that combines artificial intelligence and data sciences to transform data into knowledge in a scientific way, and then apply to medical treatment and precision health to improve people’s health and welfare. Taiwan is known as competitive human capital especially for information and integrated circuit technology, together with the advanced medical systems, high-coverage national health insurance research database, a wealth of electronic health records, and the government open data. Academia Sinica and our Institute well suit to devote to smart health because of our key features: cross-disciplinary experts in artificial intelligence and data science, well trained young talents (AS-NTU Data Science Program, TIGP Bioinformatics Program, and Academia Sinica Data Science Statistical Cooperation Center), integrative interfaces of software and hardware infrastructures, rich data resources (Taiwan Biobank and Taiwan Precision Medicine Initiative) and biomedical collaborations, as well as the newly established National Biotechnology Research Park. This project is led by PI Director Chun-houh Chen and four co-PIs: Da-Wei Wang (Institute of Information Science), Wen-Chang Lin (Institute of Biomedical Sciences), Yuh-Shan Jou (Institute of Biomedical Sciences), and Hsin-Chou Yang (Institute of Statistical Science).


計畫目的 Project objectives

  • Form a multidisciplinary smart health team of artificial intelligence and data sciences to provide an integrative research interface and promote the real applications in smart health.
  • Establish a network with the major medical centers in Taiwan for collaborative research and the development of real products in smart health
  • Develop novel analytical technologies, strategies, and platforms in smart health especially in the following areas:
    1. Various types and huge amounts of curated data with high quality (e.g., Taiwan Biobank, Taiwan Precision Medicine Initiative, national health research insurance data, and other health-related databases).
    2. Big data analytics in medical imaging (e.g., brain images for neurodegenerative disorders, X-ray images for lung cancer, ultra sound images for medical screening).
    3. Big data analytics in genomic and precision medicine (e.g., identification of disease susceptibility genes and chromosomal abnormality and risk prediction).
    4. Integrative analysis of multi-omics, environmental and behavioral sciences (e.g., integrative analysis of genomics, transcriptomics, metabolomics, microbiomics, life styles and nutrition intake).
    5. Big data analytics related to infectious disease and viruses (e.g., COVID-19).