A WEB-Based Version of MedLEE: A Medical Language Extraction and Encoding System

By Carol Friedman, L. Shagina, S. A. Socratous, Xiaojun Zeng. 1996

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

1. Monica Agrawal
2. David A. Sontag
3. A. Levy
4. Arvind Satyanarayan
5. Sebastian Nowozin
6. Marinka Zitnik
7. Ahmed M. Alaa
8. Nanyun Peng

Citations in the corpus

• Natural Language Processing for EHR-Based Computational Phenotyping (2018, neutral): “MedLEE [81] to detect colorectal cancer-related concepts.”
• Assessing the Impact of Automated Suggestions on Decision Making: Domain Experts Mediate Model Errors but Take Less Initiative (2021, methodology): “concept extraction systems include cTAKES, MetaMap, MedLEE, and scispaCy [2, 10, 32, 42].”
• Comparing deep learning and concept extraction based methods for patient phenotyping from clinical narratives (2018, unknown): “Examples of systems that employ such databases and extract concepts from text are the KnowledgeMap Concept Identifier (KMCI) [44], MetaMap [45], Medlee [46], MedEx [47], and the cTAKES.”