End-End Systems

STAGES - Machine Translation (MT):
Funded by the NSF.

STAGES is a joint machine translation project that involving Brandeis University, Columbia University, Information Science Institute at the University of Southern California, The University of Colorado at Boulder, and The University of Rochester.


CLAMR - Cross-Linguistic AMR's


MiPACQ:
Funded by the NIH.
Multi-source Integrated Platform for Answering Clinical Questions

MiPACQ is a question answering project. It is a project designed to build a system through which doctors can ask a computer questions about existing medical records and information sources to get answers quickly and efficiently. This project is a joint effort of the University of Colorado at Boulder, the Mayo Clinic, and the Harvard School.


EPIC:
Funded by the NSF.

Project EPIC applies natural language processing techniques in order to facilitate computer mediated communication during times of crisis.


SHARP:
Funded by HHS

The SHARP project at Colorado aims to merge and standardize patient data from non-electronic forms, such as the free text of radiology and pathology notes, into an electronic health record (EHR). The project applies natural language processing techniques to extract structured information from clinical notes that allows the information contained there to be searched, e.g. for a diagnosis, compared, e.g. to find common co-morbidities with a certain diagnosis, and summarized. The project will help improve patient care by reducing inconsistencies in patient data, providing physicians with more accurate and uniform information in a centralized location.


THYME:
Funded by NIH

The THYME project aims to identify clinical events like diseases, symptoms and treatments and recognize their ordering along a timeline. Specifically, it aims to develop an annotation schema for temporal relations in clinical free text, create an annotated corpus of clinical text following the schema, develop new algorithms for training temporal relation discovery systems on this corpus and evaluate these systems on various use cases, including clinical notes on colon cancer and radiology reports on brain tumors.