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Creators/Authors contains: "Kafle, Sabin"

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  1. ; ; (, Information Systems Frontiers)
    null (Ed.)
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  2. ; ; (, Proceedings of IEEE 20th International Conference on Information Reuse and Integration for Data Science)
    Question Answering (QA) requires understanding queries expressed in natural languages and relevant information content to provide an answer. For closed-world QAs, information access is by means of either context texts, or a Knowledge Base (KB), or both. KBs are human-generated schematic representations of world knowledge. The representational ability of neural networks to generalize world information makes it an important component of current QA research. In this paper, we study the neural networks and QA systems in the context of KBs. Specifically, we focus on surveying methods for KB embedding, how such embeddings are integrated into the neural networks, and the role such embeddings play in improving performance across different question-answering problems. 
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  3. ; ; ; ; ; ; ; ; ; (, Proceedings of the 10th ACM International Conference on Bioinformatics, Computational Biology and Health Informatics)
    Unplanned intensive care units (ICU) readmissions and in-hospital mortality of patients are two important metrics for evaluating the quality of hospital care. Identifying patients with higher risk of readmission to ICU or of mortality can not only protect those patients from potential dangers, but also reduce the high costs of healthcare. In this work, we propose a new method to incorporate information from the Electronic Health Records (EHRs) of patients and utilize hyperbolic embeddings of a medical ontology (i.e., ICD-9) in the prediction model. The results prove the effectiveness of our method and show that hyperbolic embeddings of ontological concepts give promising performance. 
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