We proposed an approach for inpatients’ FWA (fraud, waste, and abuse) detection using multi-view data. In medical insurance, inpatients’ data consisted of daily records of clinical items they consumed and the medical conditions such as gender, age, diagnosis, length of stay, and so on. Even though hospitalized due to the same disease, the clinical daily records varied significantly. To detect the abnormal variations, our method considered to group inpatients in the view of clinical daily records, as well as in the view of their medical conditions, and detect the abnormal variations based on the mismatch of the two clustering results. To accomplish the goal, we accommodated a two-step BERT (Bidirectional Encoder Representations from Transformers) to extract features of the clinical path via the representation learning of the daily consumed items for each admission. The inpatients were then divided into different groups in view of the sequential clinical items’ consumption. We also clustered the inpatients according to their clinical conditions. And the suspicious FWA cases were defined as the ones that had a low probability of cooccurrence and were inconsistent on the properties of two fine-grouping results. We experimented with our approach in multiple insurance claim data with different kinds of surgeries to detect FWA. And 95% of the suspicious cases detected using this approach were confirmed by the medical insurance experts.

In this paper, we study the problem of leveraging dialogue agents learned from reinforcement learning (RL) that can interact with patients for automatic disease screening. This application requires efficient and effective inquiry of appropriate symptoms to make accurate diagnosis recommendations. Existing studies have tried to use RL to perform both symptom inquiry and diagnosis simultaneously, which needs to deal with a large, heterogeneous action space that affects the learning efficiency and effectiveness. To address the challenge, we propose to leverage the models learned from the dialogue data to customize the settings of the reinforcement learning for more efficient action space exploration. In particular, a supervised diagnosis model is built and involved in the definition of state and reward. We also develop the clustering method to form a hierarchy in the action space. These customizations can make the learning task focus on checking the most relevant symptoms, which effectively boost the confidence of diagnosis. Besides, a novel hierarchical reinforcement learning framework with the pretraining strategy is used to reduce the dimension of action space and help the model to converge. For empirical evaluations, we conduct extensive experiments on both synthetic and real-world datasets. The results have demonstrated the superiority of our approach in diagnostic accuracy and interaction efficiency compared with other baseline methods.

Anomaly detection in healthcare data like patient records is no trivial task. The anomalies in these datasets are often caused by mismatches between different types of feature, e.g., medications that do not match with the diagnoses. Existing anomaly detection methods do not perform well when detecting "mismatches" between multiple types of feature, especially when the feature space is high-dimensional and sparse. This paper introduces a novel anomaly detection paradigm: Probabilistic-Mismatch Anomaly Detection (PMAD), which detects mismatches between features by modeling a normal instance with a common latent probability distribution that governs the generation of all types of feature. Under this paradigm, the target of anomaly detection is to find instances with dissimilar latent distributions. We further propose Topical PMAD based on an extended Latent Dirichlet Allocation (LDA) model, which is able to capture the latent relationship between features in a high-dimensional space. Experiments on both synthetic data and real-world patient records show that Topical PMAD can effectively detect anomalies with mismatched features, and is highly robust against high-dimensional data as well as inaccurate model selection. The real-world anomalies detected on a patient record dataset show a promising application prospect.

We show that existing mature, relational optimizers can be exploited with a novel schema to give better performance for property graph storage and retrieval than popular noSQL graph stores. The schema combines relational storage for adjacency information with JSON storage for vertex and edge attributes. We demonstrate that this particular schema design has benefits compared to a purely relational or purely JSON solution. The query translation mechanism translates Gremlin queries with no side effects into SQL queries so that one can leverage relational query optimizers. We also conduct an empirical evaluation of our schema design and query translation mechanism with two existing popular property graph stores. We show that our system is 2-8 times better on query performance, and 10-30 times better in throughput on 4.3 billion edge graphs compared to existing stores.