In recent years, with the better availability of medical data such as Electronic Health Records (EHR), more and more data mining models have been developed to explore the data-driven insights for better human health. However, there are many challenges for analyzing EHR such as high-dimensionality, temporality, sparsity, etc., which make the data-driven models less reliable. Medical knowledge graph (MKG), which encodes comprehensive knowledge about the medical concepts and relationships extracted from medical literature, holds great promise to regularize the data-driven models as prior knowledge. Nonetheless, the MKGs are typically not complete, which limits its utility in helping with the data mining process. In this paper, we propose a mutual enhancement framework MendMKG for predictive modeling of clinical events with both EHR and MKG. In particular, MendMKG first conducts a self-supervised learning strategy to simultaneously pre-train a graph attention network for embedding nodes and complete the MKG. It iteratively performs (1) an embedding-based knowledge graph completion module to derive missing edges, (2) and a reconstruction module of unlabeled EHR data to select high-quality ones from these edges, which would be further appended to the MKG to update the embedding model. Through the iterations, the two modules mutually benefit each other. Then, MendMKG uses the pre-trained graph attention network and the updated MKG to generate the visit embeddings to represent patient’s historical visits, and predict the diagnosis in future visit, through a fine-tuning approach. Experimental results on real world EHR corpus are provided to demonstrate the superiority of the proposed framework, compared to a series of state-of-the-art baselines.11The source code and knowledge graph data have been anonymously uploaded to https://github.com/1317375434/MendMKG.

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.

Phrase mining is a fundamental task for text analysis and has various downstream applications such as named entity recognition, topic modeling, and relation extraction. In this paper, we focus on mining high-quality phrases from domain-specific corpora with special consideration of infrequent ones. Previous methods might miss infrequent high-quality phrases in the candidate selection stage. And these methods rely on explicit features to mine phrases while rarely considering the implicit features. In addition, completeness is rarely explicitly considered in the evaluation of a high-quality phrase. In this paper, we propose a novel approach that exploits a sequence labeling model to capture infrequent phrases. And we employ implicit semantic features and contextual POS tag statistics to measure meaningfulness and completeness, respectively. Experiments over four real-world corpora demonstrate that our method achieves significant improvements over previous state-of-the-art methods across different domains and languages.

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.

A Care Pathway is a knowledge-centric process to guide clinicians to provide evidence-based care to patients with specific conditions. One existing problem for care pathways is that they often fail to reflect the best clinical practice as a result of not being adequately updated. A better understanding of the gaps between a care pathway and real practice requires aligning patient records with the pathway. Patient records are unlabeled in practice making it difficult to align them with a care pathway which is inherently complex due to its representation as a hierarchical and declarative process model (HDPM). This paper proposes to solve this problem by developing a Hierarchical Markov Random Field (HMRF) method so that a set of patient records can best fit a given care pathway. We validate the effectiveness of the method with experiments on both synthesized data and real clinical data.

Ontology matching, aiming to obtain semantic correspondences between two ontologies, has played a key role in data exchange, data integration and metadata management. Among numerous matching scenarios, especially the applications cross multiple domains, we observe an important problem, denoted as unbalanced ontology matching which requires to find the matches between an ontology describing a local domain knowledge and another ontology covering the information over multiple domains, is not well studied in the community.

With the proliferation of data APIs, it is not uncommon that users who have no clear ideas about data APIs will encounter difficulties to build Mashups to satisfy their requirements. In this paper, we present a semantic-based mashup navigation system, sMash that makes mashup building easy by constructing and visualizing a real-life data API network. We build a sample network by gathering more than 300 popular APIs and find that the relationships between them are so complex that our system will play an important role in navigating users and give them inspiration to build interesting mashups easily. The system is accessible at: http://www.dart.zju.edu.cn/mashup.

We present sMash, a system for facilitating users to mashup Web data. The aspects emphasized by the demo are: (1) how to help novice users master data APIs and relationships amongst them easily; (2) how to inspire various users to build more amazing Web data mashups. First, a real-life data API network is constructed and visualized to enable users to surf and mashup. Second, two kinds of recommendations are generated dynamically based on a comprehensive analysis of the network, user's traces and a repository of mashups to provide navigation.