MORPHER  A Platform to Support Modeling of Outcome and Risk Prediction in Health Research Machine learning is rapidly becoming a mainstay in research and industry. Particularly for clinical predictive modeling, these approaches are being increasingly applied, as evidenced by the growth in the number of related publications. While different computer tools exist that support rapid prototyping, we observe that the state of the art is lacking in the extent to which the needs of research clinicians are addressed. This leads to an increase in the time needed for development and validation of such models. In this paper, we outline the requirements and challenges inherent to this domain and present a platform for rapid prototyping tailored to the specific needs of clinical modeling for outcome and risk prediction. We argue that a move towards hybrid solutions, i.e., a mix of cloud and onpremise infrastructure, constitutes a viable way to reduce the time needed to develop and validate clinical predictive models in a standardized, reproducible fashion.

KnowRisk: An Interpretable KnowledgeGuided Model for Disease Risk Prediction Thanks to the widespread adoption of Electronic Health Record (EHR) systems, a variety of datadriven 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 knowledgeguided 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, skipconnections and attention mechanisms are adopted to improve the interpretability of our model. In the context of heart failure prediction task, our model outperforms several stateoftheart methods. Finally, a series of case studies are presented to prove the interpretability of our model.