View source for NLP Tools (I2B2 Project)

Textual medical records contain need to be extracted and/or indexed in order to be analyzed and interpreted by automated tools. A collection of natural language processing (NLP) tools to extract various types of information from unstructured medical records. Although a number of NLP systems had demonstrated good accuracy in information extraction, they were often domain-, institution- and application-specific.

The generic NLP components, when assembled in pipelines and initialized with custom configuration parameters, become a powerful medical data mining instrument. Medical concepts such as diagnoses, comorbidities, discharge medications, and smoking status can be ascertained. A textual medical record is a rich source of clinical information.

A suite of NLP tools for the I2B2 (Informatics for Integrating Biology and the Bedside, a national center for biomedical computing) project to address a wide range of text processing needs.

A modularized and parameterized approach in the software development and employed syntactic, statistical, template-based methods for different parsing tasks. This approach allows users to tailor the NLP tools to extract and index specific information from different domains and institutions.

==Methods==
I2B2 developed 11 modules for text report processing

[[File:NLP components for medical report processing.jpg|frameless]]

# Section Splitter
# Section Filter
# Text Tokenizer
# Part-of-Speech (POS) Tagger
# Noun Phrase Finder
# UMLS Concept Finder
# Negation Finder
# Regular Expression-based Concept Finder
# Sentence Splitter
# N-Gram Tool
# Classifier (e.g. Smoking Status Classifier)

==Resources==
<ref>https://www.i2b2.org/NLP/DataSets/Main.php</ref>
<ref>https://www.i2b2.org/software/</ref>


[[Category:Epitrial]]
[[Category:Healthcare]]
[[Category:Technology]]