The BioMedical Information Collection and Understanding System
BioMedICUS is a system for large-scale text analysis and processing of biomedical and clinical reports. The system is being developed by the Natural Language Processing and Information Extraction Program at the University of Minnesota Institute for Health Informatics.
- Scalability and performance. We use BioMedICUS to process millions of notes here at the University of Minnesota. To do this we need BioMedICUS to have high throughput and to support both machine-level and distributed parallelization.
- Usability. We try to minimize dependencies and prerequisites that BioMedICUS requires. We release under the permissive Apache 2.0 license and pay close attention to intellectual property issues.
BioMedICUS has an RTF Reader, which has the ability to read and process notes that are encoding in RTF. In addition, BioMedICUS uses RTF formatting information downstream to improve other components.
Included in the standard pipeline is an acronym detector, which has the ability to detect and expand acronyms to their equivalent long forms.
BioMedICUS includes a fast concept detector which labels instances of UMLS Metathesaurus concepts in text.
Contact and Support
For issues or enhancement requests, feel free to submit to the Issues tab on GitHub.
BioMedICUS has a gitter chat for contacting developers with questions, suggestions or feedback.
BioMedICUS is developed by the University of Minnesota Institute for Health Informatics NLP/IE Group with assistance from the Open Health Natural Language Processing (OHNLP) Consortium.
Funding for this work was provided by:
- 1 R01 LM011364-01 NIH-NLM
- 1 R01 GM102282-01A1 NIH-NIGMS
- U54 RR026066-01A2 NIH-NCRR
The following people have made code contributions not represented in the commit history:
- Robert Bill
- Arun Kumar
- Serguei Pakhomov
- Yan Wang