Temporal-spatial Correlation Attention Network for Clinical Data
  Analysis in Intensive Care Unit
Weizhi Nie Yuhe Yu Chen Zhang Dan Song Lina Zhao Yunpeng Bai
In recent years, medical information technology has made it possible for
electronic health record (EHR) to store fairly complete clinical data. This has
brought health care into the era of "big data". However, medical data are often
sparse and strongly correlated, which means that medical problems cannot be
solved effectively. With the rapid development of deep learning in recent
years, it has provided opportunities for the use of big data in healthcare. In
this paper, we propose a temporal-saptial correlation attention network (TSCAN)
to handle some clinical characteristic prediction problems, such as predicting
death, predicting length of stay, detecting physiologic decline, and
classifying phenotypes. Based on the design of the attention mechanism model,
our approach can effectively remove irrelevant items in clinical data and
irrelevant nodes in time according to different tasks, so as to obtain more
accurate prediction results. Our method can also find key clinical indicators
of important outcomes that can be used to improve treatment options. Our
experiments use information from the Medical Information Mart for Intensive
Care (MIMIC-IV) database, which is open to the public. Finally, we have
achieved significant performance benefits of 2.0\% (metric) compared to other
SOTA prediction methods. We achieved a staggering 90.7\% on mortality rate,
45.1\% on length of stay. The source code can be find:
\url{https://github.com/yuyuheintju/TSCAN}.