no code implementations • 11 Apr 2020 • Lu-chen Liu, Zequn Liu, Haoxian Wu, Zichang Wang, Jianhao Shen, Yiping Song, Ming Zhang
Mortality prediction of diverse rare diseases using electronic health record (EHR) data is a crucial task for intelligent healthcare.
no code implementations • 12 Nov 2019 • Zichang Wang, Haoran Li, Lu-chen Liu, Haoxian Wu, Ming Zhang
Most related studies transform EHR data of a patient into a sequence of clinical events in temporal order and then use sequential models to learn patient representations for outcome prediction.
no code implementations • 14 Oct 2019 • Lu-chen Liu, Haoxian Wu, Zichang Wang, Zequn Liu, Ming Zhang
Rather than directly applying the LSTM model to the event sequences, our proposed model firstly aggregates heterogeneous clinical events in a short period and then captures temporal interactions of the aggregated representations with LSTM.
no code implementations • 20 Mar 2019 • Lu-chen Liu, Haoran Li, Zhiting Hu, Haoran Shi, Zichang Wang, Jian Tang, Ming Zhang
Our model learns hierarchical representationsof event sequences, to adaptively distinguish between short-range and long-range events, and accurately capture coretemporal dependencies.
1 code implementation • 13 Mar 2018 • Lu-chen Liu, Jianhao Shen, Ming Zhang, Zichang Wang, Jian Tang
One important application is clinical endpoint prediction, which aims to predict whether a disease, a symptom or an abnormal lab test will happen in the future according to patients' history records.