Towards Interpretable Clinical Diagnosis with B ayesian Network Ensembles Stacked on Entity-Aware CNN s (2020)

Abstract: The automatic text-based diagnosis remains a challenging task for clinical use because it requires appropriate balance between accuracy and interpretability. In this paper, we attempt to propose a solution by introducing a novel framework that stacks Bayesian Network Ensembles on top of Entity-Aware Convolutional Neural Networks (CNN) towards building an accurate yet interpretable diagnosis system. The proposed framework takes advantage of the high accuracy and generality of deep neural networks as well as the interpretability of Bayesian Networks, which is critical for AI-empowered healthcare. The evaluation conducted on the real Electronic Medical Record (EMR) documents from hospitals and annotated by professional doctors proves that, the proposed framework outperforms the previous automatic diagnosis methods in accuracy performance and the diagnosis explanation of the framework is reasonable.

## introduction the automatic diagnosis of diseases has drawn the increasing attention from both research communities and industrial companies in the recent years due to the advancement of artificial intelligence (ai) @xcite @xcite . as reported in @xcite , "ai-enabled analysis software is helping to guide doctors and other health-care workers through diagnostic processes and questioning to arrive at treatment decisions with greater speed and accuracy." although the image-based diagnosis has been well studied using pacs (picture archiving and communication systems) data @xcite , the text-based diagnosis for clinical decision support (cds) @xcite remains difficult due to the rare access to reliable clinical corpus and the difficulty in balancing between accuracy and interpretability. there have been attempts to study automatic text-based diagnosis with electronic medical record (emr) documents integrated in the hospital information system @xcite @xcite . basically, an emr document is written by a doctor and consists of several sections that describe the illness of the patient. besides the patient's basic information like name, age and gender, an emr document contains chief complaint (cc), history of present illness (hpi), physical examination (pe), test reports (tr, e.g. lab test reports and pacs reports), diagnosis, etc. in this study, we bring forward a novel framework of automatic diagnosis with emr documents for cds. 1 specifically, we propose to predict the main diagnosis based on the patient's current illness. different from the previous works @xcite @xcite @xcite that solely rely on the end-to-end neural models, we propose to stack the bayesian network (bn) ensembles on top of entity-aware convolutional neural networks (ecnn) in automatic diagnosis, where ecnn improves the accuracy of the prediction and bn ensembles explain the prediction. the proposed framework attempts to bring some interpretability of the predictions by incorporating the knowledge encoded in the bn ensembles. the main contributions of this work are as follows: ## related work due to the rapid advancement of machine intelligence, the text-based automatic diagnosis is becoming one of the most important applications of machine learning and natural language processing in the recent years @xcite . different from diagnosis or question answering on the web @xcite , diagnosis for the cds takes place in the hospitals and clinics, and the predictive algorithm is integrated into the hospital information system to assist doctors and physicians in the diagnosis. @xcite proposes a top-down hierarchical classification method towards diagnosing pediatric diseases. from the root to the leaf, each level on the diagnostic hierarchy is a logistic regression model that performs classification on labels from coarse granularity to fine-grained granularity, e.g. from organ systems down to respiratory systems and to upper respiratory systems. this method requires heavy manual annotation of training samples at different levels of hierarchy. @xcite combines the variational auto-encoder and the variational recurrent neural network together to make diagnosis based on laboratory test data. however, laboratory test data are not the only resources considered in this paper. @xcite introduces the memory networks into diagnostic inference based on free text clinical records with external knowledge source from wikipedia. @xcite proposes a hierarchical gru-based neural network to predict the clinical outcomes based on the medical code sequences of the patient's previous visits. it deals with the sequential disease forecasting problem with ehr data rather than the diagnosis problem for the current visit with emr document. similarly, @xcite studies the rnn-based model for clinical event prediction. @xcite investigates the multi-label classification problem for discharge summaries of ehr with hierarchical attention-bidirectional gru. the most similar works to ours are in @xcite which trains an endto-end convolutional network model to predict di-agnosis based on emrs. besides, @xcite improves the cnn model with the attention mechanism in automatic diagnosis. moreover, @xcite studies a label-wise attention model to further improve the accuracy of diagnosis at the cost of more computation time. @xcite proposes a reverse time attention mechanism for interpretable healthcare studies. different from the previous studies, the novelty of this paper is to bring interpretability into automatic diagnosis by stacking the ensembles of bayesian networks on top of the entity-aware convolutional neural networks. ## the proposed framework automatic diagnosis can be formally considered as a classification problem where the proposed method outputs a probability distribution pr(d|s) over all diseases @xmath0. in this study, s corresponds to the patient's emr document, i.e. s consists of several sections of texts and some structured data like age, gender and medical department. we bring forward a new framework that combines the black-box deep learning and the whitebox knowledge inference to diagnose disease with emr documents. figure 1 shows the architecture of the proposed framework. firstly, the medical entities are extracted from the emr contents. then, the emr document is fed into the entity-aware convolutional networks to generate disease prior probability. next, the bayesian network ensembles perform disease inference based on the prior probability and the probabilistic graphical models (pgms) before ensembling the final predictions. ## experiments and results in this section, we will introduce the data sets we experiment with and the evaluation results. ## conclusion in this paper, we investigate the problem of automatic diagnosis with emr documents for clinical decision support. we propose a novel framework that stacks the bayesian network ensembles on top of the entity-aware convolutional neural networks. the proposed design brings interpretability into the predictions, which is very important for the ai-empowered healthcare, without compromising the accuracy of convolutional networks. the evaluation conducted on the real emr documents from hospitals validates the effectiveness of the proposed framework compared to the baselines in automatic diagnosis with emr.