Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network.

Journal: Artificial intelligence in medicine
Published Date:

Abstract

BACKGROUND AND OBJECTIVE: Clinical decision support assisted by prediction models usually faces the challenges of limited clinical data and a lack of labels when the model is developed with data from a single medical institution. Accordingly, research on multicenter clinical collaborative networks, which can provide external medical data, has received increasing attention. With the increasing availability of machine learning techniques such as transfer learning, leveraging large-scale patient data from multiple hospitals to build data-driven predictive models with clinical application potential provides an alternative solution to address the problem of limited patient data.

Authors

  • Jin Li
    Mental Health Center, West China Hospital, Sichuan University, Chengdu, China.
  • Yu Tian
    Key Laboratory of Development and Maternal and Child Diseases of Sichuan Province, Department of Pediatrics, Sichuan University, Chengdu, China.
  • Runze Li
    Department of Statistics, The Pennsylvania State University, University Park, PA 16802, United States. Electronic address: rzli@psu.edu.
  • Tianshu Zhou
    Engineering Research Center of EMR and Intelligent Expert System, Ministry of Education, Collaborative Innovation Center for Diagnosis and Treatment of Infectious Diseases, College of Biomedical Engineering and Instrument Science, Zhejiang University, Hangzhou Zhejiang Province, China.
  • Jun Li
    Department of Emergency, Zhuhai Integrated Traditional Chinese and Western Medicine Hospital, Zhuhai, 519020, Guangdong Province, China. quanshabai43@163.com.
  • Kefeng Ding
    Department of Surgical Oncology, The Second Affiliated Hospital of Zhejiang University Medical School, Hangzhou, China.
  • Jingsong Li
    Research Center for Healthcare Data Science, Zhejiang Lab, Hangzhou, China.