Extraction of Tumor Response Criteria in Semi-Structured Imaging Report.

In this study, we extracted information from 6,376 french CT scan semi-structured text reports evaluating the cancer treatment response using the RECIST methodology. We evaluated the performance against manual annotation of 100 reports and measured the evolution of the presence of information over time. The results show high performances of the extraction as well as trends.

Can reproducibility be improved in clinical natural language processing? A study of 7 clinical NLP suites.

Background: The increasing complexity of data streams and computational processes in modern clinical health information systems makes reproducibility challenging. Clinical natural language processing (NLP) pipelines are routinely leveraged for the secondary use of data. Workflow management systems (WMS) have been widely used in bioinformatics to handle the reproducibility bottleneck.

Natural Language Processing for Rapid Response to Emergent Diseases: Case Study of Calcium Channel Blockers and Hypertension in the COVID-19 Pandemic.

Background: A novel disease poses special challenges for informatics solutions. Biomedical informatics relies for the most part on structured data, which require a preexisting data or knowledge model; however, novel diseases do not have preexisting knowledge models. In an emergent epidemic, language processing can enable rapid conversion of unstructured text to a novel knowledge model.

An architecture for genomics analysis in a clinical setting using Galaxy and Docker.

Next-generation sequencing is used on a daily basis to perform molecular analysis to determine subtypes of disease (e.g., in cancer) and to assist in the selection of the optimal treatment.