Explainable Prediction of Medical Codes from Clinical Text

Clinical notes are text documents that are created by clinicians for each patient encounter. They are typically accompanied by medical codes, which describe the diagnosis and treatment. Annotating these codes is labor intensive and error prone; furthermore, the connection between the codes and the text is not annotated, obscuring the reasons and details behind specific diagnoses and treatments. We present an attentional convolutional network that predicts medical codes from clinical text. Our method aggregates information across the document using a convolutional neural network, and uses an attention mechanism to select the most relevant segments for each of the thousands of possible codes. The method is accurate, achieving precision@8 of 0.71 and a Micro-F1 of 0.54, which are both better than the prior state of the art. Furthermore, through an interpretability evaluation by a physician, we show that the attention mechanism identifies meaningful explanations for each code assignmentComment: NAACL 201

Order-free Medicine Combination Prediction with Graph Convolutional Reinforcement Learning

Medicine Combination Prediction (MCP) based on Electronic Health Record (EHR) can assist doctors to prescribe medicines for complex patients. Previous studies on MCP either ignore the correlations between medicines (i.e., MCP is formulated as a binary classifcation task), or assume that there is a sequential correlation between medicines (i.e., MCP is formulated as a sequence prediction task). The latter is unreasonable because the correlations between medicines should be considered in an order-free way. Importantly, MCP must take additional medical knowledge (e.g., Drug-Drug Interaction (DDI)) into consideration to ensure the safety of medicine combinations. However, most previous methods for MCP incorporate DDI knowledge with a post-processing scheme, which might undermine the integrity of proposed medicine combinations. In this paper, we propose a graph convolutional reinforcement learning model for MCP, named Combined Order-free Medicine Prediction Network (CompNet), that addresses the issues listed above. CompNet casts the MCP task as an order-free Markov Decision Process (MDP) problem and designs a Deep Q Learning (DQL) mechanism to learn correlative and adverse interactions between medicines. Specifcally, we frst use a Dual Convolutional Neural Network (Dual-CNN) to obtain patient representations based on EHRs. Then, we introduce the medicine knowledge associated with predicted medicines to create a dynamic medicine knowledge graph, and use a Relational Graph Convolutional Network (R-GCN) to encode it. Finally, CompNet selects medicines by fusing the combination of patient information and the medicine knowledge graph. Experiments on a benchmark dataset, i.e., MIMIC-III, demonstrate that CompNet signifcantly outperforms state-of-the-art methods and improves a recently proposed model by 3.74%pt, 6.64%pt in terms of Jaccard and F1 metrics