Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease progression prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, an object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient’s current diseases or risk factors and stops at a disease entity representing the predicted disease. The trajectory generated by the object represents an interpretable disease progression path of the given patient. The dynamics of the object are controlled by a policy-based reinforcement learning module, which is trained by electronic health records (EHRs). Experiments: We utilized three real-world EHR datasets to evaluate the performance of our model. In the disease progression prediction task, our model achieves 0.743, 0.639, and 0.643 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the three datasets, respectively. This performance is comparable to medical research’s commonly used machine learning models. In qualitative analysis, our clinical collaborator reviewed the disease progression paths generated by our model and advocated their interpretability and reliability. Conclusion: Experimental results validate the proposed model in interpretably evaluating and optimizing disease progression prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.

Survival analysis exhibits profound effects on health service management. Traditional approaches for survival analysis have a pre-assumption on the time-to-event probability distribution and seldom consider sequential visits of patients on medical facilities. Although recent studies leverage the merits of deep learning techniques to capture non-linear features and long-term dependencies within multiple visits for survival analysis, the lack of interpretability prevents deep learning models from being applied to clinical practice. To address this challenge, this article proposes a novel attention-based deep recurrent model, named AttenSurv , for clinical survival analysis. Specifically, a global attention mechanism is proposed to extract essential/critical risk factors for interpretability improvement. Thereafter, Bi-directional Long Short-Term Memory is employed to capture the long-term dependency on data from a series of visits of patients. To further improve both the prediction performance and the interpretability of the proposed model, we propose another model, named GNNAttenSurv , by incorporating a graph neural network into AttenSurv, to extract the latent correlations between risk factors. We validated our solution on three public follow-up datasets and two electronic health record datasets. The results demonstrated that our proposed models yielded consistent improvement compared to the state-of-the-art baselines on survival analysis.

Massively available longitudinal data about long-term disease trajectories of patients provides a golden mine for the understanding of disease progression and efficient health service delivery. It calls for quantitative modeling of disease progression, which is a tricky problem due to the complexity of the disease progression process as well as the irregularity of time documented in trajectories. In this study, we tackle the problem with the goal of predictively analyzing disease progression. Specifically, we propose a novel Variational Hawkes Process (VHP) model to generalize disease progression and predict future patient states based on the clinical observational data of past disease trajectories. First, Hawkes Process captures the intensity of irregular visits in a trajectory documented to medical facilities and controls the aforementioned information flowing into future visits. Thereafter, the captured intensity is incorporated into a Variational Auto-Encoder to generate the representation of the future partial disease trajectory for a target patient in a predictive manner. To further improve the prediction performance, we equip the proposed model with a disease trajectory discriminator to distinguish the generated trajectories from real ones. We evaluate the proposed model on two public datasets from the MIMIC-III database pertaining to heart failure and sepsis patients, respectively, and one real-world dataset from a Chinese hospital pertaining to heart failure patients with multiple admissions. Experimental results demonstrate that the proposed model significantly outperforms state-of-the-art baselines, and may derive a set of practical implications that can benefit a wide spectrum of management and applications on disease progression.

Healthcare process leaves patient treatment trajectory (PTT), described as a sequence of interdependent clinical events affiliated with a large volume of longitudinal therapy and treatment information. Predicting the future clinical event in PTT, as a vital and essential task for providing insights into the entire treatment trajectory, can serve as an efficient and proactive altering service for health service delivery. However, it is challenging because there are long-term dependencies between clinical events, which are irregularly distributed along the temporal axis with varying time intervals. This characteristic inevitably impedes the performance of clinical event prediction (CEP) using the existing approaches. To address this challenge, we propose a novel approach to learn representative and discriminative PTT features for CEP. In detail, multivariate Hawkes process (HP) is adopted to uncover the mutual excitation intensities between clinical event pairs in an interpretable manner. Thereafter, the captured spontaneous and interactional intensities of events are incorporated into recurrent neural networks (RNN) to encode PTT in latent representations, while jointly performing the CEP task based on the extracted trajectory representations. We evaluate the performance of the proposed approach on a real clinical dataset consisting of 13,545 visits of 2,102 heart failure patients. Compared to state-of-the-art methods, our best model achieves 6.4% and 4.1% AUC performance gains on three-months and one-year CEP tasks, respectively. The experimental results demonstrate that the proposed approach outperforms state-of-the-art models in CEP, and can be profitably exploited as a basis for PTT analysis and optimization.

Acute coronary syndrome (ACS), as an emergent and severe syndrome due to decreased blood flow in the coronary arteries, is a leading cause of death and serious long-term disability globally. ACS is usually caused by one of three problems: ST elevation myocardial infarction, non-ST elevation myocardial infarction, or unstable angina. Major adverse cardiac event (MACE) prediction, as a critical tool to estimate the likelihood an individual is at risk of ACS, has been widely adopted in the early prevention and intervention of ACS. Although valuable, existing MACE prediction models are designed to predict the overall probability of MACE occurrence for ACS patients, and lack the ability to look for insight into the disease to distinguish the different subtypes of ACS in a fine-grained manner. It is interesting to exploit the different subtypes of ACS and mine their private and shared underlying knowledge to improve the performance of MACE prediction. In this study, we propose utilizing a large volume of heterogeneous electronic health records for the application of MACE prediction. In detail, we address the multi-subtype-oriented MACE prediction for ACS as a multi-task learning (MTL) problem, present a MTL-based model to predict MACE of ACS patients with the different subtypes, and incorporate adversarial learning into the model to alleviate both the shared and private latent feature spaces of each subtype of ACS from interfering with each other. A real clinical dataset containing 2,863 ACS patient samples is collected from a Chinese hospital to validate the proposed model. Experimental results demonstrate that the prediction performance of our proposed model obtains a significant improvement, compared to single-subtype-oriented MACE prediction models.

Clinical pathways leave traces, described as event sequences with regard to a mixture of various latent treatment behaviors. Measuring similarities between patient traces can profitably be exploited further as a basis for providing insights into the pathways, and complementing existing techniques of clinical pathway analysis (CPA), which mainly focus on looking at aggregated data seen from an external perspective. Most existing methods measure similarities between patient traces via computing the relative distance between their event sequences. However, clinical pathways, as typical human-centered processes, always take place in an unstructured fashion, i.e., clinical events occur arbitrarily without a particular order. Bringing order in the chaos of clinical pathways may decline the accuracy of similarity measure between patient traces, and may distort the efficiency of further analysis tasks. In this paper, we present a behavioral topic analysis approach to measure similarities between patient traces. More specifically, a probabilistic graphical model, i.e., latent Dirichlet allocation (LDA), is employed to discover latent treatment behaviors of patient traces for clinical pathways such that similarities of pairwise patient traces can be measured based on their underlying behavioral topical features. The presented method provides a basis for further applications in CPA. In particular, three possible applications are introduced in this paper, i.e., patient trace retrieval, clustering, and anomaly detection. The proposed approach and the presented applications are evaluated via a real-world dataset of several specific clinical pathways collected from a Chinese hospital.