Collaborative Graph Learning with Auxiliary Text for Temporal Event Prediction in Healthcare

Accurate and explainable health event predictions are becoming crucial for healthcare providers to develop care plans for patients. The availability of electronic health records (EHR) has enabled machine learning advances in providing these predictions. However, many deep-learning-based methods are not satisfactory in solving several key challenges: 1) effectively utilizing disease domain knowledge; 2) collaboratively learning representations of patients and diseases; and 3) incorporating unstructured features. To address these issues, we propose a collaborative graph learning model to explore patient-disease interactions and medical domain knowledge. Our solution is able to capture structural features of both patients and diseases. The proposed model also utilizes unstructured text data by employing an attention manipulating strategy and then integrates attentive text features into a sequential learning process. We conduct extensive experiments on two important healthcare problems to show the competitive prediction performance of the proposed method compared with various state-of-the-art models. We also confirm the effectiveness of learned representations and model interpretability by a set of ablation and case studies.

[1]  Christopher D. Manning,et al.  Effective Approaches to Attention-based Neural Machine Translation , 2015, EMNLP.

[2]  Peter Szolovits,et al.  MIMIC-III, a freely accessible critical care database , 2016, Scientific Data.

[3]  Graham Neubig,et al.  Learning to Deceive with Attention-Based Explanations , 2020, ACL.

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

[5]  Yuan Luo,et al.  MedGCN: Graph Convolutional Networks for Multiple Medical Tasks , 2019, ArXiv.

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

[7]  Nilmini Wickramasinghe,et al.  Deepr: A Convolutional Net for Medical Records , 2016, ArXiv.

[8]  Li Li,et al.  Deep Patient: An Unsupervised Representation to Predict the Future of Patients from the Electronic Health Records , 2016, Scientific Reports.

[9]  Roman Grundkiewicz,et al.  Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing , 2015, EMNLP 2015.

[10]  Jiangtao Wang,et al.  AdaCare: Explainable Clinical Health Status Representation Learning via Scale-Adaptive Feature Extraction and Recalibration , 2019, AAAI.

[11]  Jimeng Sun,et al.  MiME: Multilevel Medical Embedding of Electronic Health Records for Predictive Healthcare , 2018, NeurIPS.

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

[13]  Shanshan Zhang,et al.  Interpretable Representation Learning for Healthcare via Capturing Disease Progression through Time , 2018, KDD.

[14]  Byron C. Wallace,et al.  Attention is not Explanation , 2019, NAACL.

[15]  No Value,et al.  Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics , 2000 .

[16]  Meng Wang,et al.  Safe Medicine Recommendation via Medical Knowledge Graph Embedding , 2017, ArXiv.

[17]  Le Song,et al.  GRAM: Graph-based Attention Model for Healthcare Representation Learning , 2016, KDD.

[18]  Yujia Li,et al.  Learning the Graphical Structure of Electronic Health Records with Graph Convolutional Transformer , 2020, AAAI.

[19]  Geoffrey E. Hinton,et al.  Visualizing Data using t-SNE , 2008 .

[20]  Jimeng Sun,et al.  Pre-training of Graph Augmented Transformers for Medication Recommendation , 2019, IJCAI.

[21]  Yasha Wang,et al.  ConCare: Personalized Clinical Feature Embedding via Capturing the Healthcare Context , 2019, AAAI.