07 Apr 2014

Lab meeting on Wed 4/9 at 10:30 note new time in TASC1 9208.

A group of researchers from the BC Genome Sciences Centre will visit us this week to talk about Biomedical Text Mining and Artificial Intelligence Applied to Clinical Reporting.

The group includes Inanc Birol who is a Senior Scientist at the BC Genome Sciences Centre and Victoria Stuart who will present on the following topic as a way to guide our discussion.

We are a newly-formed group (Jan. 2014) within the GSC Bioinformatics Technology Lab that seeks to apply a combined natural language processing, text mining, computational linguistics, machine learning, artificial intelligence (NLP/TM/CL/ML/AI) approach to address the issue of the annotation of clinical reports with (i) the clinical data that is generated through our sequencing pipeline, and (ii) the relevant biomedical literature. As this is being done in a clinical setting, issues of quality and accuracy (genomic sequencing and analyses), turnaround time (speed), and standardized reporting are critically important. Our BTL-AI group is addressing the clinical reporting component by developing protocols to retrieve the biomedical literature relevant to our clinical reports, that will be provided to our end users as annotations that include quality assessments. In the broader sense, our work is relevant to the issue of information overload affecting all researchers, including the sheer volume of published material such as that provided by PubMed, that currently contains >24 million biomedical records with new records being added at a rate of ~1 million records/year. Through our project, we will develop and provide the tools needed to automatically search, retrieve, analyze, condense and focus the scientific literature - transparently returning information in an indicated domain, with confidence measures indicated. We have been reviewing the current state of the art in this topic area (NLP/TM/CL/ML/AI) and have identified leading, top-performing tools and resources developed through community efforts (e.g. The BioNLP Shared Task challenges) that we are currently bringing into our lab for evaluation and extension (customization), as needed.