A suite of natural language processing tools developed for the I2B2 project.
Year: 2006
Type: article
Abstract: Textual medical records contain a wealth of information that needs to be extracted and / or indexed in order to be analyzed and interpreted by the automated tools. We have developed a collection of natural language processing (NLP) tools to extract various types of information from unstructured medical records. The generic NLP components, when assembled in pipelines and initialized with custom configuration parameters, become a powerful medical data mining instrument. We have successfully extracted such medical concepts as diagnoses, comorbidities, discharge medications, and smoking status from various types of medical records.
Source: PubMed Central
Authors: Sergey Goryachev, Margarita Sordo, Qing T Zeng
Institutions: Brigham and Women's Hospital, Harvard University
Language: English
Subfield: Health Informatics
Field: Medicine
Domain: Health Sciences
Open Access status: green
OpenAlex ID: W1499823815