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What is MedLEE™?
MedLEE™ is a Natural Language Processing (NLP) application that extracts medical codes from
typed or dictated free text narratives and converts the data into computerized clinical information.
Why is MedLEE™ called the "Gold Standard of Natural Language Processing"?
- MedLEE™ was developed at Columbia University during 20 years of research and field testing.
- MedLEE™ automatically extracts and codes medical reports fast, inexpensively and accurately.
- It covers the biomedical domain and utilizes national and international standards (UMLS, SNOMED and ICD-9).
- It is the most widely used natural language processor in the medical research community.
- In numerous evaluations carried out by independent users MedLEE™ has shown to be as accurate as
- physicians in interpreting narrative medical records.
- MedLEE™ has received US and international patents.
Track Record
- MedLEE™ has been sucessfully used by government agencies (DoD, NCI), evaluated and recommended by the
VA, and successfully deployed at major medical centers in Pittsburgh, Chicago, Cincinnati, New York and others.
- In a biosurveillance project for the City of New York, MedLEE™ detected patient records identifying
individuals at high risk for having active tuberculosis and reduced the rate of missed tuberculosis findings
by almost one half at New York Presbyterian Hospital.
- MedLEE™ was used to code 889,921 radiographic reports on 251,186 patients. Its processor's sensitivity
and specificity were comparable to those previously reported for expert coders.
MedLEE™ Benefits Sumamry
- Proven accuracy across all major medical domains.
- Most cost effective medical natural language processor for billing, reimbursement and decision support.
- MedLEE™'s multiple knowledge component structure allows users to select output formats (HL7, SML) to adjust pre- and post-processor parameters and to easily add or modify lexica.
- Real-time interface to speech recognition.
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MedLEE, MedLEE NLP, NLP, Natural Language Processing, Text Mining, Core
Measures, Healthcare IT, PQRI, Infection Surveillance, Patient summary data,
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Oncology NLP, CAPS Extraction, Semantic Search
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