计算机科学
相似性(几何)
结果(博弈论)
机器学习
人工智能
任务(项目管理)
电子健康档案
健康档案
深度学习
医疗保健
经济增长
图像(数学)
经济
数理经济学
管理
数学
作者
Fuqiang Yu,Lizhen Cui,Yiming Cao,Ning Liu,Weiming Huang,Yonghui Xu
标识
DOI:10.1007/978-3-031-00126-0_31
摘要
The rapid growth in the use of electronic health records (EHR) offers promises for predicting patient outcomes. Previous works on this task focus on exploiting temporal patterns from sequential EHR data. Nevertheless, such approaches model patients independently, missing out on the similarities between patients, which are crucial for patients’ health risk assessment. Moreover, they fail to capture the fine-grained progression of patients’ status, which assist in inferring the patients’ future status. In this work, we propose a similarity-aware collaborative learning model SiaCo for patient outcome prediction. In particular, we design two similarity measurers and two global knowledge matrices to separately calculate the similarity of patients with different information levels and support collaborative learning between patients. To capture the more fine-grained progression of patients’ status, we design a parallelized LSTM to model the temporal-dependent patterns of patient status. Finally, SiaCo integrates the information learned from two measures and the parallelized LSTM to predict patient outcomes. Extensive experiments are conducted on two real disease datasets. The experimental results demonstrate that SiaCo outperforms the state-of-the-art models for two typical tasks of patient outcome prediction.
科研通智能强力驱动
Strongly Powered by AbleSci AI