A Suite of Natural Language Processing Tools Developed for the. I2B2 Project ... they were often domain-, institution- and application- specific. We have ... Finder. List of. Matching. Concepts. Section. Filter. Text. Tokenizer. Sentence. Splitter.
A Suite of Natural Language Processing Tools Developed for the I2B2 Project Sergey Goryachev, MS, Margarita Sordo, Ph.D., Qing T. Zeng, Ph.D. Decision Systems Group, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA 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.
INTRODUCTION
Medical Report
Syntactic Parsing-based Method Section Splitter
Section Filter
A textual medical record is a rich source of clinical information. Although a number of NLP systems had demonstrated good accuracy in information extraction, they were often domain-, institution- and applicationspecific. We have developed 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. We took 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 AND RESULTS We have developed 11 modules for text report processing (Figure 1): 1. Section Splitter 2. Section Filter 3. Text Tokenizer 4. Part-of-Speech (POS) Tagger 5. Noun Phrase Finder 6. UMLS Concept Finder 7. Negation Finder 8. Regular Expression-based Concept Finder 9. Sentence Splitter 10. N-Gram Tool 11. Classifier (e.g. Smoking Status Classifier) These modules were applied to discharge summaries and outpatient notes from 2 institutions, Brigham and Women’s Hospital and Massachusetts General Hospital with minimum changes in the configuration files. They were also used to extract key data items from a set of medical error reports, which involved adding several new modules, but didn’t require any alteration of the original 11 modules.
Textual Medical Records
Text Tokenizer
Part-ofSpeech Tagger
Noun Phrases Finder
1
2
3
4
5
Template-based Method Regular Expressionbased Concept Finder
Collection of report sections
Discharge Medications
Statistical Method Sentence Splitter
Collection of tagged tokens
For example:
List of Matching Concepts
Collection of report sections matching some criteria
Collection of tokens (words)
8
9
10
Collection of sentences
N-Gram Tool
11
N-Gram frequencies
Classifier
For example:
UMLS Concept Finder
6
7
Collection of noun phrases
Classification
smoker, nonsmoker, past smoker, etc
Collection of UMLS concepts
Negation Finder
UMLS Concepts with Negation
For example:
Diagnoses or Comorbidities
Figure 1. NLP components for medical report processing assembled into pipelines for various information extraction tasks.
AMIA 2006 Symposium Proceedings Page - 931
