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Related Concept Videos

Discharge Summary Forms01:31

Discharge Summary Forms

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The discharge summary is crucial as it enables a smooth transition from a healthcare facility to a patient's home or another care setting. This critical document facilitates seamless continuity of care, ensuring patients receive the necessary support and attention.
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The meaning of illness is individualized to each person who experiences an alteration in health. In contrast, disease is a medical term indicating a pathological change in the structure and function of the body or mind. It is a condition that has specific symptoms and boundaries.
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Learning to Classify Medical Discharge Summaries According to ICD-9.

Leonardo Moros1, Jérôme Azé1, Sandra Bringay1,2

  • 1LIRMM UMR 5506, University of Montpellier, CNRS, Montpellier, France.

Studies in Health Technology and Informatics
|May 19, 2023
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Summary
This summary is machine-generated.

This study introduces a new post-hoc method to enhance International Classification of Diseases (ICD) code classification recall. The approach calibrates code counts, achieving a 20% recall improvement over standard methods.

Keywords:
NLPSupervised learningconstrained optimization

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Area of Science:

  • Medical Informatics
  • Machine Learning
  • Clinical Natural Language Processing

Background:

  • Improving the accuracy of International Classification of Diseases (ICD) code assignment is crucial for healthcare analytics.
  • Current classification methods often struggle with optimizing the number of codes per document, impacting diagnostic recall.

Purpose of the Study:

  • To present a novel post-hoc approach for enhancing ICD classification recall.
  • To develop a method that calibrates the number of codes returned per document.

Main Methods:

  • The proposed method functions as a post-hoc layer, adaptable to any existing classification model.
  • It focuses on adjusting the output to optimize code prediction.
  • The approach was validated on a stratified split of the MIMIC-III dataset.

Main Results:

  • The post-hoc calibration method significantly improved classification recall.
  • Achieved a 20% increase in recall compared to a classic classification approach when returning an average of 18 codes per document.

Conclusions:

  • The presented post-hoc strategy offers a viable method to boost ICD classification performance.
  • This approach enhances diagnostic code recall without requiring changes to the underlying classifier.