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Language Development01:22

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Children master language quickly and with relative ease, supported by both biological predisposition and reinforcement. B. F. Skinner (1957) proposed that language is learned through reinforcement, while Noam Chomsky (1965) argued that language acquisition mechanisms are biologically determined.
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Developing a portable natural language processing based phenotyping system.

Himanshu Sharma1, Chengsheng Mao2, Yizhen Zhang2

  • 1Cyberinfrastructure, University of Illinois at Chicago, Chicago, IL, 60612, USA.

BMC Medical Informatics and Decision Making
|April 5, 2019
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Summary
This summary is machine-generated.

This study introduces a portable phenotyping system for extracting clinical information from text. It enables consistent application of rule-based and machine learning methods across different data systems, improving data standardization.

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Area of Science:

  • Clinical informatics
  • Natural Language Processing (NLP)
  • Machine Learning

Background:

  • Developing portable clinical phenotyping systems is challenging due to data variability across institutions.
  • Existing rule-based and machine learning approaches often lack interoperability.

Purpose of the Study:

  • To present a portable phenotyping system integrating rule-based and machine learning methods.
  • To enhance data standardization and portability across diverse healthcare data systems.

Main Methods:

  • Utilized Unified Medical Language System (UMLS) for feature extraction from unstructured text.
  • Incorporated Observational Health Data Sciences and Informatics (OHDSI) Common Data Model (CDM) for data standardization.
  • Stored rule-based system components (e.g., regular expressions) in OMOP CDM format for reusability.

Main Results:

  • Successfully performed portable phenotyping of obesity and 15 comorbidities from patient discharge summaries.
  • Achieved performance ranking within the top 10 participants in the i2b2 Obesity Challenge pilot study.

Conclusions:

  • The developed system enables consistent application of NLP classification techniques across disparate datasets.
  • Standardization through OMOP CDM facilitates the reuse, adaptation, and extension of clinical NLP systems.
  • The system promotes interoperability and portability of phenotyping across institutions and data systems.