Natural Language Processing

Award Information
Agency: Department of Defense
Branch: Army
Contract: W81XWH-07-C-0077
Agency Tracking Number: O063-H09-3116
Amount: $99,435.00
Phase: Phase I
Program: SBIR
Awards Year: 2007
Solicitation Year: 2006
Solicitation Topic Code: OSD06-H09
Solicitation Number: 2006.3
Small Business Information
9500 Innovation Drive, Manassas, VA, 20110
DUNS: 945837219
HUBZone Owned: N
Woman Owned: N
Socially and Economically Disadvantaged: N
Principal Investigator
 Gary Sikora
 Principal Investigator
 (703) 368-6107
Business Contact
 Christine Sigety
Title: Business Management
Phone: (703) 368-6107
Research Institution
We propose a hybrid NLP solution: a medical ontology system from Mayo Clinic, the Mayo Vocabulary Server (MVS) based on SNOMED- CT, MEDCIN and many other UMLS terminologies; and a machine learning system from Carnegie Mellon University (CMU), the Scone semantic knowledge-base that represents knowledge from numerous ontologies and corpuses. Out-of-the-box, the MVS far exceeds the Phase I goals, with a proven recall sensitivity of 99.7%, precision of 99.8% and specificity of 97.9%, using a 5,000 inpatient and the outpatient published data sample set. While the MVS provides a great vertical depth within the medical domain, the Scone horizontally broadens the knowledge into other domains, increasing the realm possibilities. For example, an NLP input to Scone linked to a news feed can automatically understand an event such as a “forest fire in Alaska” and the concept that “fire causes smoke”. The MVS then can be used to inference about smoke and return the concept that “smoke triggers asthma”. This and additional inferences about health and local information can conclude expected inpatient volume, helping first responders in natural and homeland security scenarios. NLP input from speech recognition systems are also addressed to provide real-time prompts to improve medical form inputs.

* Information listed above is at the time of submission. *

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