Timely detection of clinical events would provide healthcare providers the opportunity to make meaningful interventions that can result in improved health outcomes. This work describes a methodology developed at a large U.S. healthcare insurance company for predicting clinical events using administrative claims data. Most of the existing literature for predicting clinical events leverage historical data in Electronic Health Records (EHR). EHR data however has limitations making it undesirable for real-time use-cases. It is inconsistent, expensive, inefficient and sparsely available. In contrast, administrative claims data is relatively consistent, efficient and readily available. In this work, we introduce a novel modeling workflow: First, we learn custom embeddings for medical codes within claims data in order to uncover the hidden relationships between them. Second, we introduce a novel way of representing a member’s health history with a graph such that the relationships between various diagnosis and procedure codes is captured. Finally, we apply Graph Neural Networks (GNN) to perform a multi-label graph classification for clinical event prediction. Our approach produces more accurate predictions than any other standard classification approaches and can be easily generalized to other clinical prediction tasks.

In the insurance industry, timely and effective interaction with customers are at the core of everyday operations and processes that are key for a satisfactory customer experience. These interactions often result in sequences of data derived from events that occur over time. Such recurrent patterns can provide valuable information that can be used in a variety of ways to improve customer related work-flows. In this paper we demonstrate the application of a recently proposed algorithm to uncover such time patterns that takes into account the time between events to form such patterns. We use temporal customer data generated from two different use-cases (satisfaction and fraud) to show that this algorithm successfully detects patterns that occur in the insurance context.

Recommender systems have become extremely important to various types of industries where customer interaction and feedback is paramount to the success of the business. For companies that face changes that arise with ever-growing markets, providing product recommendations to new and existing customers is a challenge. Furthermore, it is important to have an algorithm which is descriptive, scalable, agnostic to missing features, and robust in providing these recommendations. Directed graphical models meet all these demands; however, if the dimensionality of the features is high, structure learning and inference can become computationally prohibitive. In this work, we propose an algorithm with some novel aspects to learn the structure of a graphical model (e.g. Bayesian network), which considerably speeds up both training (from days to minutes in some cases) and inference run-times with respect to standard Bayesian structure learning approaches, while achieving similar accuracy. We also show that this approach produces more accurate predictions than a state-of-the-art matrix factorization algorithm in the absence of complete evidence on several insurance-related datasets.