Contextualized Relevance Feedback for Precision Medicine Precision Medicine (PM) is viewed 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, aiming at providing medical evidence to the pointofcare. Previous PM approaches are mostly based on unigram matching of individual query terms, or concepts, to the target articles to produce the ranking list, while ignoring the context of the matched query terms of concepts. To this end, this paper presents a preliminary investigation of utilizing contextualized representation of text for pseudo relevance feedback (PRF) to enhance PM search effectiveness. By considering the multiaspect word relations, we propose a  BERT_NPRF  model to integrate PRF with the finetuned BERT model for contextualized interaction of documentdocument pairs. Experimental results on the standard Text REtrieval Conference (TREC) PM track benchmark show that our proposed method with interpolation can improve the performance in PM.

When the How Outweighs the What: the Pivotal Importance of Context A growing body of knowledge about biological mechanisms and interaction of biological components is contained in the peerreviewed scientific literature. In order to leverage this knowledge towards the development of predictive models, one must first extract these relationships from the text. However, the context in which the interaction was reported is critical in ensuring that it is used in a manner consistent with the modelu0027s intended application. Here we assess the applicability of two generic automated methods for leveraging a broader contextual structure in the more specific domain of a biological experiment using only the paperu0027s title and abstract. In an example use case, a Support Vector Machine (SVM) and two variants of the broadlyused Bidirectional Encoder Representations from Transformers (BERT) neural network model, serve to distinguish mouse from human subject experiments in a corpus of over 12,000 papers documenting mechanistic interactions in a regulatory model of of mucosal immune signaling. The BERT and domainspecific BioBERT yielded essentially equivalent classification accuracy with both neural network models performing only marginally better than the SVM. Words occurring frequently in abstracts were largely nonspecific, whereas words unique to each class were used in 4% or less of the abstracts. These highspecificity words were used in very similar contexts that separated mouse and human study abstracts on the basis of study design and experimental procedure rather than species or basic biological markers.