Abstract. Electronic medical records contain multi-format electronic medical data that consist of an abundance of medical knowledge. Facing with patients symptoms, experienced caregivers make right medical decisions based on their professional knowledge that accurately grasps relationships between symptoms, diagnosis and corresponding treatments. In this paper, we aim to capture these relationships by constructing a large and high-quality heterogenous graph linking patients, diseases, and drugs (PDD) in EMRs. Specifically, we propose a novel framework to extract important medical entities from MIMIC-III (Medical Information Mart for Intensive Care III) and automatically link them with the existing biomedical knowledge graphs, including ICD-9 ontology and DrugBank. The PDD graph presented in this paper is accessible on the Web via the SPARQL endpoint, and provides a pathway for medical discovery and applications, such as effective treatment recommendations.
In this paper, we extend our previously published hybrid analytical model which is for the estimation of shielding effectiveness of a dual-cavity structure with an aperture array to generalize the model for a wider range of applications. In the proposed model, the aperture array in the center, off-center, higher-order modes, and multi-cavity structure are taken into consideration, respectively. At last, a comparison of the results calculated by the extended hybrid analytical model with those obtained by the simulation software CST is given. The results show that the extended hybrid analytical model for the shielding effectiveness prediction of a three-cavity structure with numerous apertures has a higher precision and a higher efficiency.
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