Thanks to the huge accumulation of Electronic Health Records (EHRs), numerous deep learning based predictive models were proposed for this task. Among them, most of the existing state-of-the-art (SOTA) models were built with recurrent neural networks (RNNs). Regardless of their success, RNN-based models mainly suffer from three limitations. (i) Accuracy: the prediction accuracy of RNN-based models drops quickly as the length of EHR sequences increases. (ii) Efficiency: the recurrence property of RNN-based models makes the computation parallelization impossible, and accordingly hurts the efficiency of such models in practice. (iii) Interpretability: the outputs of RNN-based models are difficult to explain due to the unexplainable nature of deep models. In this paper, we resort to the recently advanced attention mechanism to model the dependencies between inputs and outputs, which overcomes shortages of RNN-based models in accuracy and efficiency. As for interpretability, we model the relationships with two linear mappings from the input to the output, which account for two important factors—one is for context-aware information and the other is for time-aware representation—of capturing discriminative features in learning patient’s representations. We empirically demonstrate the effectiveness of the proposed model in both accuracy and computational efficiency, meanwhile, analyze and discuss the reasonability of each explanation approach.

Predicting drug combinations according to patients' electronic health records is an essential task in intelligent healthcare systems, which can assist clinicians in ordering safe and effective prescriptions. However, existing work either missed/underutilized the important information lying in the drug molecule structure in drug encoding or has insufficient control over Drug-Drug Interactions (DDIs) rates within the predictions. To address these limitations, we propose CSEDrug, which enhances the drug encoding and DDIs controlling by leveraging multi-faceted drug knowledge, including molecule structures of drugs, Synergistic DDIs (SDDIs), and Antagonistic DDIs (ADDIs). We integrate these types of knowledge into CSEDrug by a graph-based drug encoder and multiple loss functions, including a novel triplet learning loss and a comprehensive DDI controllable loss. We evaluate the performance of CSEDrug in terms of accuracy, effectiveness, and safety on the public MIMIC-III dataset. The experimental results demonstrate that CSEDrug outperforms several state-of-the-art methods and achieves a 2.93% and a 2.77% increase in the Jaccard similarity scores and F1 scores, meanwhile, a 0.68% reduction of the ADDI rate (safer drug combinations), and 0.69% improvement of the SDDI rate (more effective drug combinations).

Patient similarity measurement is an essential step in discovering clinically meaningful subgroups and building case retrieval systems. Most existing studies implement this procedure using similarity measurement algorithms on the multivariate clinical time-series (input space) or the low-dimensional patient representation (representation space) learned by a representation learning model. However, they either suffer from the adverse effects of irrelevant variables in the data or fail to assess the fine-grained similarity underneath the disease progress. In this paper, we propose a method to measure more fine-grained patient similarity in the state space, where each patient is represented by a series of state representations that reveal the dynamic health status. We discuss three desiderata, including stability, personality, and interpretability, for the state representations, and on this basis, develop a supervised predictive model that learns good state representations for identifying similar patients and predicting patient outcomes. Experimental results on the publicly available dataset MIMIC-III show that our method offers a promising direction for precisely identifying similar patients at the state trajectory level, as well as accurately predicting outcomes.

Clinical time-series is receiving long-term attention in data mining and machine learning communities and has boosted a variety of data-driven applications. Identifying similar patients or subgroups from clinical time-series is an essential step to design tailored treatments in clinical practice. However, most of the existing methods are either purely unsupervised that tend to neglect the patient outcome information or cannot generate personalized patient representation through supervised learning, thus may fail to identify ‘truly similar patients’ (i.e., patients who similar in both outcomes and individual outcome-related clinical variables). To tackle these limitations, we propose a novel predictive clinical time-series analysis framework. Specifically, our framework uses task-specific information to rule out the task-irrelevant factors in each patient data individually and generates the contribution scores that reveal the factors’ importance for the patient outcome. Then a patient representation construction method is proposed to generate task-related and personalized representations by combining remained factors and their contribution scores. At last, similarity measurement or cluster analysis can be conducted. We evaluate our framework on three real-world clinical time-series datasets, empirically demonstrate that our framework achieves improvements in prediction performance, similarity measurement, and clustering, thus potentially benefiting patient-similarity-based precision medicine applications.

Deep learning has proved to be a powerful tool for medical image analysis in recent years. Data imbalance is a common problem in medical images. Dice Loss is widely used in medical image segmentation tasks to address the data imbalance problem. However, it only addresses the imbalance problem between foreground and background yet overlooks another imbalance between easy and hard examples that also severely affects the training process of a learning model. Empirically speaking, an easy example generally contributes less to the overall loss than a hard example. However, in practice, compared with hard examples, a large number of easy examples will be generated from a medical image and will dominate the training model, resulting in sub-optimal training or worse. To tackle this problem, we propose a novel Focal Dice Loss to alleviate the imbalance between hard examples and easy examples. Focal Dice Loss is able to reduce the contribution from easy examples and make the model focus on hard examples through our proposed novel balanced sampling strategy during the training process. Furthermore, to evaluate the effectiveness of our proposed loss functions, we conduct extensive experiments on two real-world medical image datasets with 2D and 3D convolutional neural networks. The experimental results show that our proposed Focal Dice Loss brings a significant improvement in segmentation performance compared to Dice Loss. Moreover, we find that our proposed Focal Dice Loss can effectively alleviate the over-fitting problem.

Building a predictive model based on historical Electronic Health Records (EHRs) for personalized healthcare has become an active research area. Benefiting from the powerful ability of feature extraction, deep learning (DL) approaches have achieved promising performance in many clinical prediction tasks. However, due to the lack of interpretability and trustworthiness, it is difficult to apply DL in real clinical cases of decision making. To address this, in this paper, we propose an interpretable and trustworthy predictive model~(INPREM) for healthcare. Firstly, INPREM is designed as a linear model for interpretability while encoding non-linear relationships into the learning weights for modeling the dependencies between and within each visit. This enables us to obtain the contribution matrix of the input variables, which is served as the evidence of the prediction result(s), and help physicians understand why the model gives such a prediction, thereby making the model more interpretable. Secondly, for trustworthiness, we place a random gate (which follows a Bernoulli distribution to turn on or off) over each weight of the model, as well as an additional branch to estimate data noises. With the help of the Monto Carlo sampling and an objective function accounting for data noises, the model can capture the uncertainty of each prediction. The captured uncertainty, in turn, allows physicians to know how confident the model is, thus making the model more trustworthy. We empirically demonstrate that the proposed INPREM outperforms existing approaches with a significant margin. A case study is also presented to show how the contribution matrix and the captured uncertainty are used to assist physicians in making robust decisions.

Predicting the future health conditions of patients based on Electronic Health Records (EHR) is an important research topic. Due to the temporal nature of EHR data, the major challenge is how to properly model the sequences of patient visits. Recurrent Neural Networks (RNNs) with attention mechanisms are widely employed to address this challenge, but often vulnerable to data insufficiency. Lately, predictive models with the guidance of medical knowledge have been proposed to solve this problem and achieve superior performance. Although these models learn reasonable embeddings (infused with knowledge) for clinical variables, they are not able to fully make use of the underlying information in the knowledge graph. To address this, we propose an end-to-end robust solution, namely Graph Neural networks based Diagnosis Prediction (GNDP), to predict future conditions for patients. Compared with existing methods, GNDP learns the spatial and temporal patterns from patients' sequential graph, in which the domain knowledge is naturally infused. We evaluate our GNDP model against a set of state-of-the-art methods on two real-world EHR datasets and the results demonstrate that our approach significantly outperforms the baseline methods.

Medical images are widely used in the medical domain for the diagnosis and treatment of diseases. Reading a medical image and summarizing its insights is a routine, yet nonetheless time-consuming task, which often represents a bottleneck in the clinical diagnosis process. Automatic report generation can relieve the issues. However, generating medical reports presents two major challenges: (i) it is hard to accurately detect all the abnormalities simultaneously, especially the rare diseases; (ii) a medical image report consists of many paragraphs and sentences, which are longer than natural image captions. We present a new framework to accurately detect the abnormalities and automatically generate medical reports. The report generation model is based on hierarchical recurrent neural network (HRNN). We introduce a topic matching mechanism to HRNN, so as to make generated reports more accurate and diverse. The soft attention mechanism is also introduced to HRNN model. Experimental results on two image-paragraph pair datasets show that our framework outperforms all the state-of-art methods.

Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of data-driven clinical risk prediction approaches have been spawned in recent years. However, there remain three challenges, which if addressed would improve the performance and applicability of such models. (i) Due to the limited data sharing between different health care institutions, the EHR data collected by a single institution is often inadequate or missing some visits records. The limited number of data cannot meet the large sample required of recent approaches especially deep learning models. In addition, the missing records (due to visiting different institution) may contain important health condition of the patient, which if ignored would cause prediction bias. (ii) Few existing approaches take clinical knowledge into account. The auxiliary knowledge if included can greatly reduce the data dependency of many modern learning algorithms. (iii) Most existing deep learning based methods are unable to identify the contribution of each medical event to the final results, which prohibits such models from being widely accepted in practical clinical applications. In this paper, we propose an interpretable and knowledge-guided deep model to address these challenges. Specifically, we distill knowledge from a clinical knowledge graph both explicitly and implicitly, which can not only supplement inadequate patient records but also guide the predicting process of the model. Furthermore, skip-connections and attention mechanisms are adopted to improve the interpretability of our model. In the context of heart failure prediction task, our model outperforms several state-of-the-art methods. Finally, a series of case studies are presented to prove the interpretability of our model.