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Clinical decision support system (CDSS) plays a significant role nowadays and it assists physicians in making decisions for treatment. Generally based on clinical guideline, the principles of the recommendation are provided and may suggest several candidate medications for similar patient group with certain clinical conditions. However, it is challenging to prioritize these candidates and even refine the guideline to a finer level for patient-specific recommendation. Here we propose a method and system to integrate the clinical knowledge and real-world evidence (RWE) for type 2 diabetes treatment, to enable both standardized and personalized medication recommendation. The RWE is generated by medication effectiveness analysis and subgroup analysis. The knowledge model has been verified by clinical experts from the advanced hospitals. The data verification results show that the medications that are consistent with the method recommendation can lead to better clinical outcome in terms of glycemic control, compared to those inconsistent.

Background Fraud, Waste, and Abuse (FWA) detection is a significant yet challenging problem in the health insurance industry. An essential step in FWA detection is to check whether the medication is clinically reasonable with respect to the diagnosis. Currently, human experts with sufficient medical knowledge are required to perform this task. To reduce the cost, insurance inspectors tend to build an intelligent system to detect suspicious claims with inappropriate diagnoses/medications automatically. Objective The aim of this study was to develop an automated method for making use of a medical knowledge graph to identify clinically suspected claims for FWA detection. Methods First, we identified the medical knowledge that is required to assess the clinical rationality of the claims. We then searched for data sources that contain information to build such knowledge. In this study, we focused on Chinese medical knowledge. Second, we constructed a medical knowledge graph using unstructured knowledge. We used a deep learning–based method to extract the entities and relationships from the knowledge sources and developed a multilevel similarity matching approach to conduct the entity linking. To guarantee the quality of the medical knowledge graph, we involved human experts to review the entity and relationships with lower confidence. These reviewed results could be used to further improve the machine-learning models. Finally, we developed the rules to identify the suspected claims by reasoning according to the medical knowledge graph. Results We collected 185,796 drug labels from the China Food and Drug Administration, 3390 types of disease information from medical textbooks (eg, symptoms, diagnosis, treatment, and prognosis), and information from 5272 examinations as the knowledge sources. The final medical knowledge graph includes 1,616,549 nodes and 5,963,444 edges. We designed three knowledge graph reasoning rules to identify three kinds of inappropriate diagnosis/medications. The experimental results showed that the medical knowledge graph helps to detect 70% of the suspected claims. Conclusions The medical knowledge graph–based method successfully identified suspected cases of FWA (such as fraud diagnosis, excess prescription, and irrational prescription) from the claim documents, which helped to improve the efficiency of claim processing.

Atrial fibrillation (AF) is associated with an increased risk of acute ischemic stroke (AIS). Accurately predicting AIS and planning effective treatment pathways for AIS prevention are crucial for AF patients. Because of the temporality of patients' disease progressions, sequential disease and treatment patterns have the potential to improve risk prediction performance and contribute to effective treatment pathways. This paper integrates temporal pattern mining into the AF study of AIS prediction and treatment pathway discovery. We combine temporal pattern mining with feature selection to identify temporal risk factors that have predictive ability, and integrate temporal pattern mining with treatment efficacy analysis to discover temporal treatment patterns that are statistically effective. Results show that our approach has identified new potential temporal risk factors for AIS that can improve the prediction performance, and has discovered treatment pathway patterns that are statistically effective to prevent AIS for AF patients.

To solve the shared tasks of COIN: COmmonsense INference in Natural Language Processing) Workshop in , we need explore the impact of knowledge representation in modeling commonsense knowledge to boost performance of machine reading comprehension beyond simple text matching. There are two approaches to represent knowledge in the low-dimensional space. The first is to leverage large-scale unsupervised text corpus to train fixed or contextual language representations. The second approach is to explicitly express knowledge into a knowledge graph (KG), and then fit a model to represent the facts in the KG. We have experimented both (a) improving the fine-tuning of pre-trained language models on a task with a small dataset size, by leveraging datasets of similar tasks; and (b) incorporating the distributional representations of a KG onto the representations of pre-trained language models, via simply concatenation or multi-head attention. We find out that: (a) for task 1, first fine-tuning on larger datasets like RACE (Lai et al., 2017) and SWAG (Zellersetal.,2018), and then fine-tuning on the target task improve the performance significantly; (b) for task 2, we find out the incorporating a KG of commonsense knowledge, WordNet (Miller, 1995) into the Bert model (Devlin et al., 2018) is helpful, however, it will hurts the performace of XLNET (Yangetal.,2019), a more powerful pre-trained model. Our approaches achieve the state-of-the-art results on both shared task’s official test data, outperforming all the other submissions.