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Precision medicine (PM) is regarded as an information retrieval (IR) task in which biomedical articles containing treatment information about specific diseases or genetic variants are retrieved in response to patient record for the purpose of providing medical evidence to the point-of-care. In existing PM approaches, manual keywords, such as “treatment” and “therapy,” are considered direct indicators of treatment information and are thereby introduced to expand the original query. However, the common medical concepts that are implicitly related to treatment (such as “oncogene” and “tumor”), and differ the relevant documents from the non-relevant ones, are yet to be utilized. To bridge the gap, in this paper, we propose an extension of the state-of-the-art neural IR (NIR) models, including K-NRM and DRMM, to encapsulate the PM solutions within a neural network framework, referred to as NIR $^{PM}$. Specifically, the proposed approach mines a global list of common medical concepts from documents that are judged pertinent to different queries. Thereafter, the mined implicit concepts are incorporated within an NIR framework to enhance the effectiveness of PM. The experimental results on the standard Text REtrieval Conference (TREC) PM track benchmark confirm the superior performance of the proposed NIRPM model. [ABSTRACT FROM AUTHOR]

The multitemporal interferometric synthetic aperture radar (InSAR) technique is a potential tool for measuring digital elevation models and surface deformation. It has the advantage of high precision, competitive spatial resolution, and wide coverage. To improve the accuracy of the final results, some adaptive multilook strategies have been proposed in which the identification of statistically homogeneous pixels (SHPs) is the key task. However, these methods are not always reliable in the case of small data sets. To improve this reliability, SHPs are identified based on the adaptive joint data vector comprising of temporal sample and spatial information in this letter. Additionally, the formulation of adaptive joint data vector is combined with local spatial features of SAR images. The presented adaptive multilook approach can be used in many interferometric applications, such as InSAR data filtering and coherence estimation. Experiments on six TerraSAR-X stripmap images of Tianjin in China validate the feasibility and effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]