CTC-Attention based Non-Parametric Inference Modeling for Clinical State Progression

Predictive modeling of patient state to state medical conditions in ICU is a critical yet challenging task in health informatics and machine learning. Prior critical stages from the same ICU admission may contribute differently to the next stages. That said, stages are interdependent, and disease progression is a multi-step temporal observation. In this paper, we formally name this problem as “Clinical State Progression Prediction (CSPP).” Conventional temporal modeling may fit well to predictions of fixed size observations and number of stages, but have troubles and less flexibility when addressing CSPP. To that end, we cast this problem as multi-label learning on time series data in which each stage is marked by a label. The implementation of entire framework includes two phases. In learning, an RNN based Encoder-Decoder deep model is developed for basic temporal modeling. In addition, Attention mechanism and Connectionist Temporal Classification (CTC) are integrated to explicitly model the temporal dependency as well as monotonic relation between input time series and output label space. In inference, based on the observed multi-stage labels, a non-parametric retrieval is carried out first to build up the reference patient records. Then, based on CTC-Attention learning model, consistent progressions are computed and ranked to contribute to the prediction of the clinical state progression in the next few hours. Extensive experiments on MIMIC III and Parkinson datasets demonstrate that the proposed predictive modeling for CSPP outperforms state-of-the-art works on Sepsis, Kidney-Sepsis-Mortality, Heart-Sepsis-Mortality, and Parkinson Progression.