Baseline and extensions approach to information retrieval of complex medical data: Poznan's approach to the bioCADDIE 2016

A2A: a platform for research in biomedical literature search

BACKGROUND

Finding relevant literature is crucial for many biomedical research activities and in the practice of evidence-based medicine. Search engines such as PubMed provide a means to search and retrieve published literature, given a query. However, they are limited in how users can control the processing of queries and articles-or as we call them documents-by the search engine. To give this control to both biomedical researchers and computer scientists working in biomedical information retrieval, we introduce a public online tool for searching over biomedical literature. Our setup is guided by the NIST setup of the relevant TREC evaluation tasks in genomics, clinical decision support, and precision medicine.

RESULTS

To provide benchmark results for some of the most common biomedical information retrieval strategies, such as querying MeSH subject headings with a specific weight or querying over the title of the articles only, we present our evaluations on public datasets. Our experiments report well-known information retrieval metrics such as precision at a cutoff of ranked documents.

CONCLUSIONS

We introduce the A2A search and benchmarking tool which is publicly available for the researchers who want to explore different search strategies over published biomedical literature. We outline several query formulation strategies and present their evaluations with known human judgements for a large pool of topics, from genomics to precision medicine.

Clinical trial search: Using biomedical language understanding models for re-ranking

Bidirectional Encoder Representations from Transformers (BERT) have achieved state-of-the-art effectiveness in some of the biomedical information processing applications. We investigate the effectiveness of these techniques for clinical trial search systems. In precision medicine, matching patients to relevant experimental evidence or prospective treatments is a complex task which requires both clinical and biological knowledge. To assist in this complex decision making, we investigate the effectiveness of different ranking models based on the BERT models under the same retrieval platform to ensure fair comparisons. An evaluation on the TREC Precision Medicine benchmarks indicates that our approach using the BERT model pre-trained on scientific abstracts and clinical notes achieves state-of-the-art results, on par with highly specialised, manually optimised heuristic models. We also report the best results to date on the TREC Precision Medicine 2017 ad hoc retrieval task for clinical trial search.