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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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Applying natural language processing techniques to develop a task-specific EMR interface for timely stroke

Sheng-Feng Sung1, Kuanchin Chen2, Darren Philbert Wu3

  • 1Division of Neurology, Department of Internal Medicine, Ditmanson Medical Foundation Chiayi Christian Hospital, Chiayi City, Taiwan; Department of Information Management and Institute of Healthcare Information Management, National Chung Cheng University, Chiayi County, Taiwan; Department of Nursing, Min-Hwei Junior College of Health Care Management, Tainan, Taiwan.

International Journal of Medical Informatics
|March 4, 2018
PubMed
Summary
This summary is machine-generated.

An enhanced electronic medical record (EMR) interface using natural language processing (NLP) significantly reduced errors in determining eligibility for intravenous thrombolytic therapy (IVT) in stroke patients. This improves acute stroke care by providing clinicians with timely, relevant information for decision-making.

Keywords:
Acute ischemic strokeElectronic medical recordIntravenous thrombolysisNatural language processing

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Neurology

Background:

  • Intravenous thrombolytic therapy (IVT) is critical for acute stroke care.
  • Accurate and timely eligibility determination for IVT is essential to minimize patient risk.
  • Current electronic medical record (EMR) systems may present challenges in efficiently extracting relevant patient data for IVT decisions.

Purpose of the Study:

  • To develop and evaluate an enhanced, task-specific EMR interface utilizing natural language processing (NLP).
  • To reduce errors in determining patient eligibility for intravenous thrombolytic therapy (IVT).
  • To improve the efficiency and accuracy of clinical decision-making in acute stroke care.

Main Methods:

  • Developed an NLP-powered information processing algorithm using MetaMap and the Unified Medical Language System Metathesaurus.
  • Created a task-specific EMR interface to highlight IVT-relevant concepts extracted from clinical notes.
  • Assessed clinical usability by comparing clinician performance with the new interface versus the current EMR system.

Main Results:

  • The NLP algorithm achieved high precision (0.998) and recall (0.812) at the phrase level for identifying IVT-relevant concepts.
  • Clinicians using the enhanced EMR interface demonstrated significantly higher accuracy (91%) in assessing IVT eligibility compared to the current interface (80%, p=0.016).
  • The time to complete eligibility assessment was statistically similar between the two interfaces (1.70 min vs. 2.46 min).

Conclusions:

  • The NLP-enhanced EMR interface significantly reduces errors in assessing IVT eligibility criteria.
  • This technology offers a promising approach to improve acute stroke care by facilitating IVT decision-making.
  • Presenting timely and meaningful information to clinicians enhances their ability to provide optimal stroke treatment.