Full-texts representation with Medical Subject Headings, and co-citations network rerank- ing strategies for TREC 2014 Clinical Decision Support Track

Abstract In TREC 2014 Clinical Decision Support Track, the task was to retrieve full-texts relevant for answering generic clinical questions about medical records. For this purpose, we investigated a large range of strategies in the five runs we officially submitted. Concerning Information Retrieval (IR), we tested two different indexing levels: documents or sections. Section indexing was clearly below (-40% in R-Precision). In the domain of Information Extraction, we enriched documents with Medical Subject Headings concepts that were collected from MEDLINE or extracted in the text with exact match strategies. We also investigated a target-specific semantic enrichment: MeSH terms representing diagnosis, treatments or tests (relying on UMLS semantic types) were used both in collection and in queries to guide the retrieval. Unfortunately, the MeSH representation was not as complementary with the text as we expected, and the results were disappointing. Concerning post-processing strategies, we tested the boosting of specific articles types (e.g. review articles, case reports), but the IR process already tended to favour these article types. Finally, we applied a reranking strategy relying on the cocitations network, thanks to normalized references provided in the corpus. This last strategy led to a slight improvement (+5%)

Exploiting semantics for improving clinical information retrieval

Clinical information retrieval (IR) presents several challenges including terminology mismatch and granularity mismatch. One of the main objectives in clinical IR is to fill the semantic gap among the queries and documents and going beyond keywords matching. To address these issues, in this study we attempt to use semantic information to improve the performance of clinical IR systems by representing queries in an expressive and meaningful context. In this study we propose query context modeling to improve the effectiveness of clinical IR systems. To model query contexts we propose two novel approaches to modeling medical query contexts. The first approach concerns modeling medical query contexts based on mining semantic-based AR for improving clinical text retrieval. The query context is derived from the rules that cover the query and then weighted according to their semantic relatedness to the query concepts. In our second approach we model a representative query context by developing query domain ontology. To develop query domain ontology we extract all the concepts that have semantic relationship with the query concept(s) in UMLS ontologies. Query context represents concepts extracted from query domain ontology and weighted according to their semantic relatedness to the query concept(s). The query context is then exploited in the patient records query expansion and re-ranking for improving clinical retrieval performance. We evaluate this approach on the TREC Medical Records dataset. Results show that our proposed approach significantly improves the retrieval performance compare to classic keyword-based IR model

The Gene Ontology Handbook

bioinformatics; biotechnolog

An Integrated Framework for Patent Analysis and Mining

Patent documents are important intellectual resources of protecting interests of individuals, organizations and companies. These patent documents have great research values, beneficial to the industry, business, law, and policy-making communities. Patent mining aims at assisting patent analysts in investigating, processing, and analyzing patent documents, which has attracted increasing interest in academia and industry. However, despite recent advances in patent mining, several critical issues in current patent mining systems have not been well explored in previous studies. These issues include: 1) the query retrieval problem that assists patent analysts finding all relevant patent documents for a given patent application; 2) the patent documents comparative summarization problem that facilitates patent analysts in quickly reviewing any given patent documents pairs; and 3) the key patent documents discovery problem that helps patent analysts to quickly grasp the linkage between different technologies in order to better understand the technical trend from a collection of patent documents. This dissertation follows the stream of research that covers the aforementioned issues of existing patent analysis and mining systems. In this work, we delve into three interleaved aspects of patent mining techniques, including (1) PatSearch, a framework of automatically generating the search query from a given patent application and retrieving relevant patents to user; (2) PatCom, a framework for investigating the relationship in terms of commonality and difference between patent documents pairs, and (3) PatDom, a framework for integrating multiple types of patent information to identify important patents from a large volume of patent documents. In summary, the increasing amount and textual complexity of patent repository lead to a series of challenges that are not well addressed in the current generation systems. My work proposed reasonable solutions to these challenges and provided insights on how to address these challenges using a simple yet effective integrated patent mining framework