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1.发明授权公开(公告)号：US11875882B1
公开(公告)日：2024-01-16
申请号：US18352216
申请日：2023-07-13
申请人： ZHEJIANG LAB
发明人： Jingsong Li , Feng Wang , Shengqiang Chi , Yu Tian , Tianshu Zhou
摘要： Disclosed is an system for predicting end-stage renal disease complication risk based on contrastive learning, including an end-stage renal disease data preparation module, configured to extract structured data of a patient by using a hospital electronic information system and daily monitoring equipment, and process the structured data to obtain augmented structured data; and a complication risk prediction module, configured to construct a complication representation learning model and a complication risk prediction model, perform training and learning on the augmented structured data through the complication representation learning model to obtain a complication representation, and perform end-stage renal disease complication risk prediction by using the complication representation through the complication risk prediction model.
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公开(公告)号：US11461658B2
公开(公告)日：2022-10-04
申请号：US17541298
申请日：2021-12-03
申请人： ZHEJIANG LAB
发明人： Jingsong Li , Tianshu Zhou , Ziyue Yang , Shengqiang Chi
摘要： Provided is a time series deep survival analysis system combined with active learning. The system includes: a data collection module, an active learning module, and a time series deep survival analysis module; the data collection module is used for obtaining survival data of objects to be analyzed; combined with an active learning method, the active learning module selects a part of right censored data to label a survival time; and the time series deep survival analysis module constructs a time series deep survival analysis neural network model, and takes uncensored data and right censored data as model inputs, so as to obtain survival time prediction results of the objects to be analyzed. The present application can make full use of the right censored data in the survival data and time series features.
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