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.
This is a collaborative project that aims to serve biomedical and clinical researchers, allowing for customization with different texts.
- 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.
To see what tasks the system supports, look at Components and Outputs. If you are looking for a jumping-in point, see Installation.
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.
For downloads see the releases page on GitHub. We also make more comprehensive models that require you to have a UMLS license available here.
Our wiki on GitHub contains information about installation, configuration, use, and development of BioMedICUS.
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.
NLP/IE Group Resources
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