Improving prediction for medical institution with limited patient data: Leveraging hospital-specific data based on multicenter collaborative research network
作者：
Highlights：
• A multisource deep transfer learning model is proposed for improving predictive performance on a single institution with limited patient data.
• The proposed approach enables better feature adaptation by incorporating hospital-specific features across source data and limited target data.
• The unlabeled target data is integrated into the model updating approach to enhance the training process for single center with insufficient labels.
• The case study shows the better discrimination and calibration ability of proposed model learning process than baseline models with limited EHR data.
摘要
•A multisource deep transfer learning model is proposed for improving predictive performance on a single institution with limited patient data.•The proposed approach enables better feature adaptation by incorporating hospital-specific features across source data and limited target data.•The unlabeled target data is integrated into the model updating approach to enhance the training process for single center with insufficient labels.•The case study shows the better discrimination and calibration ability of proposed model learning process than baseline models with limited EHR data.
论文关键词：Transfer learning,Data-limited settings,Distributed data mining,Model generalizability,Clinical decision support systems,Prognosis prediction
论文评审过程：Received 27 August 2020, Revised 25 November 2020, Accepted 18 January 2021, Available online 23 January 2021, Version of Record 9 February 2021.
论文官网地址：https://doi.org/10.1016/j.artmed.2021.102024
