ATTAIN: Attention-based Time-Aware LSTM Networks for Disease Progression Modeling

Modeling patient disease progression using Electronic Health Records (EHRs) is critical to assist clinical decision making. Long-Short Term Memory (LSTM) is an effective model to handle sequential data, such as EHRs, but it encounters two major limitations when applied to EHRs: it is unable to interpret the prediction results and it ignores the irregular time intervals between consecutive events. To tackle these limitations, we propose an attention-based time-aware LSTM Networks (ATTAIN), to improve the interpretability of LSTM and to identify the critical previous events for current diagnosis by modeling the inherent time irregularity. We validate ATTAIN on modeling the progression of an extremely challenging disease, septic shock, by using real-world EHRs. Our results demonstrate that the proposed framework outperforms the state-of-the-art models such as RETAIN and T-LSTM. Also, the generated interpretative time-aware attention weights shed some light on the progression behaviors of septic shock.

[1]  Charles Elkan,et al.  Learning to Diagnose with LSTM Recurrent Neural Networks , 2015, ICLR.

[2]  Adil Rafiq Rather,et al.  The Third International Consensus Definitions for Sepsis and Septic Shock (Sepsis-3) , 2015 .

[3]  Hui Li,et al.  Bone disease prediction and phenotype discovery using feature representation over electronic health records , 2015, BCB.

[4]  Joydeep Ghosh,et al.  Septic Shock Prediction for Patients with Missing Data , 2014, TMIS.

[5]  Jimeng Sun,et al.  RETAIN: An Interpretable Predictive Model for Healthcare using Reverse Time Attention Mechanism , 2016, NIPS.

[6]  Hermann Ney,et al.  LSTM Neural Networks for Language Modeling , 2012, INTERSPEECH.

[7]  Jimeng Sun,et al.  Patient Risk Prediction Model via Top-k Stability Selection , 2013, SDM.

[8]  Fei Wang,et al.  An RNN Architecture with Dynamic Temporal Matching for Personalized Predictions of Parkinson's Disease , 2017, SDM.

[9]  Volker Tresp,et al.  Predicting Clinical Events by Combining Static and Dynamic Information Using Recurrent Neural Networks , 2016, 2016 IEEE International Conference on Healthcare Informatics (ICHI).

[10]  Svetha Venkatesh,et al.  DeepCare: A Deep Dynamic Memory Model for Predictive Medicine , 2016, PAKDD.

[11]  Anuj Karpatne,et al.  Spatial Context-Aware Networks for Mining Temporal Discriminative Period in Land Cover Detection , 2019, SDM.

[12]  Xi Yang,et al.  Time-aware Subgroup Matrix Decomposition: Imputing Missing Data Using Forecasting Events , 2018, 2018 IEEE International Conference on Big Data (Big Data).

[13]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[14]  K. Wood,et al.  Duration of hypotension before initiation of effective antimicrobial therapy is the critical determinant of survival in human septic shock* , 2006, Critical care medicine.

[15]  May D. Wang,et al.  Interpretable Predictions of Clinical Outcomes with An Attention-based Recurrent Neural Network , 2017, BCB.

[16]  Chen Lin,et al.  Early Diagnosis and Prediction of Sepsis Shock by Combining Static and Dynamic Information Using Convolutional-LSTM , 2018, 2018 IEEE International Conference on Healthcare Informatics (ICHI).

[17]  Fei Wang,et al.  Health-ATM: A Deep Architecture for Multifaceted Patient Health Record Representation and Risk Prediction , 2018, SDM.

[18]  Peter J. Haug,et al.  Early Detection of Sepsis in the Emergency Department using Dynamic Bayesian Networks , 2012, AMIA.

[19]  Walter F. Stewart,et al.  Doctor AI: Predicting Clinical Events via Recurrent Neural Networks , 2015, MLHC.

[20]  Xiaowei Jia,et al.  Incremental Dual-memory LSTM in Land Cover Prediction , 2017, KDD.

[21]  Xiang Wang,et al.  Unsupervised learning of disease progression models , 2014, KDD.

[22]  Benjamin M. Marlin,et al.  Unsupervised pattern discovery in electronic health care data using probabilistic clustering models , 2012, IHI '12.

[23]  Hui Xiong,et al.  Temporal Phenotyping from Longitudinal Electronic Health Records: A Graph Based Framework , 2015, KDD.

[24]  M. Levy,et al.  Surviving Sepsis Campaign: International guidelines for management of severe sepsis and septic shock: 2008 , 2007, Intensive Care Medicine.

[25]  Fenglong Ma,et al.  Dipole: Diagnosis Prediction in Healthcare via Attention-based Bidirectional Recurrent Neural Networks , 2017, KDD.