By providing clinicians with information regarding treatment options for molecular sub-types of complex diseases with genetic origin, such as cancer, information retrieval (IR) systems play an important role in precision medicine. In this paper, we propose Bayesian Precision Medicine (BPM), a novel probabilistic framework for query expansion in information retrieval systems for Clinical Decision Support (CDS) in Precision Medicine (PM). Such systems can assist clinicians with selecting personalized treatment of complex diseases based on the patients' genomic data, such as gene mutations. In particular, we focus on a clinical decision support scenario in which clinicians provide two types of information in their queries: (1) short description of a patient's case, which may contain information regarding the type of cancer that a patient has as well as symptoms and demographics, and (2) gene mutations, which may contain gene names, mutation code and type of mutation. The goal of an IR system in this scenario is to rank biomedical articles from a large collection, such as the MEDLINE, based on their relevance to the provided query. One of the main challenges faced by IR systems in this scenario is semantic matching of heterogeneous information (gene names, medical terminology and other query keywords) in queries and relevant biomedical articles. To address this challenge, we propose a probabilistic framework that enables mapping gene mutations provided in a given query onto the biomedical concepts that are related to the entire query and can be effectively utilized for query expansion. The BPM obtains candidate query expansion concepts from biomedical knowledge bases, the Unified Medical Language System (UMLS) and the Drug-Gene Interaction Database (DGIdb), as well as the top-ranked MEDLINE articles retrieved for the original query. The BPM then utilizes information from the Catalog of Somatic Mutations in Cancer (COSMIC) and co-occurrence statistics in MEDLINE to assess the relatedness of candidate query expansion concepts to gene mutations and other information provided in a query. Experimental evaluation of the BPM was conducted on a large subset of MEDLINE articles as well as abstracts from the American Association for Cancer Research (AACR) and American Society of Clinical Oncology (ASCO) proceedings. Experimental results on a publicly available benchmark provided by the 2017 TREC precision medicine track indicate that the proposed probabilistic framework is effective at utilizing both genomic and textual information in queries to improve the accuracy of IR systems for CDS in PM through query expansion.

The problem of ad-hoc structured document retrieval arises in many information access scenarios, from Web to product search. Yet neither deep neural networks, which have been successfully applied to ad-hoc information retrieval and Web search, nor the attention mechanism, which has been shown to significantly improve the performance of deep neural networks on natural language processing tasks, have been explored in the context of this problem. In this paper, we propose a deep neural architecture for ad-hoc structured document retrieval, which utilizes attention mechanism to determine important phrases in keyword queries as well as the relative importance of matching those phrases in different fields of structured documents. Experimental evaluation on publicly available collections for Web document, product and entity retrieval from knowledge graphs indicates superior retrieval accuracy of the proposed neural architecture relative to both state-of-the-art neural architectures for ad-hoc document retrieval and probabilistic models for ad-hoc structured document retrieval.

Recent advances in deep learning and distributed representations of images and text have resulted in the emergence of several neural architectures for cross-modal retrieval tasks, such as searching collections of images in response to textual queries and assigning textual descriptions to images. However, the multi-modal retrieval scenario, when a query can be either a text or an image and the goal is to retrieve both a textual fragment and an image, which should be considered as an atomic unit, has been significantly less studied. In this paper, we propose a gated neural architecture to project image and keyword queries as well as multi-modal retrieval units into the same low-dimensional embedding space and perform semantic matching in this space. The proposed architecture is trained to minimize structured hinge loss and can be applied to both cross- and multi-modal retrieval. Experimental results for six different cross- and multi-modal retrieval tasks obtained on publicly available datasets indicate superior retrieval accuracy of the proposed architecture in comparison to the state-of-art baselines.

Although information retrieval models based on Markov Random Fields (MRF), such as Sequential Dependence Model and Weighted Sequential Dependence Model (WSDM), have been shown to outperform bag-of-words probabilistic and language modeling retrieval models by taking into account term dependencies, it is not known how to effectively account for term dependencies in query expansion methods based on pseudo-relevance feedback (PRF) for retrieval models of this type. In this paper, we propose Semantic Weighted Dependence Model (SWDM), a PRF based query expansion method for WSDM, which utilizes distributed low-dimensional word representations (i.e., word embeddings). Our method finds the closest unigrams to each query term in the embedding space and top retrieved documents and directly incorporates them into the retrieval function of WSDM. Experiments on TREC datasets indicate statistically significant improvement of SWDM over state-of-the-art MRF retrieval models, PRF methods for MRF retrieval models and embedding based query expansion methods for bag-of-words retrieval models.

Manually and automatically constructed concept graphs (or semantic networks), in which the nodes correspond to words or phrases and the typed edges designate semantic relationships between words and phrases, have been previously shown to be rich sources of effective latent concepts for query expansion. However, finding good expansion concepts for a given query in large and dense concept graphs is a challenging problem, since the number of candidate concepts that are related to query terms and phrases and need to be examined increases exponentially with the distance from the original query concepts. In this paper, we propose a two-stage feature-based method for sequential selection of the most effective concepts for query expansion from a concept graph. In the first stage, the proposed method weighs the concepts according to different types of computationally inexpensive features, including collection and concept graph statistics. In the second stage, a sequential concept selection algorithm utilizing more expensive features is applied to find the most effective expansion concepts at different distances from the original query concepts. Experiments on TREC datasets of different type indicate that the proposed method achieves significant improvement in retrieval accuracy over state-of-the-art methods for query expansion using concept graphs.