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Continuous-time systems have continuous input and output signals, with time measured continuously. These systems are generally defined by differential or algebraic equations. For instance, in an RC circuit, the relationship between input and output voltage is expressed through a differential equation derived from Ohm's law and the capacitor relation,
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Disorder recognition in clinical texts using multi-label structured SVM.

Wutao Lin1, Donghong Ji2, Yanan Lu3

  • 1School of Electronics Engineering and Computer Science, Peking University, Beijing, 100871, China.

BMC Bioinformatics
|February 2, 2017
PubMed
Summary
This summary is machine-generated.

This study introduces a multi-label structured Support Vector Machine (SVM) for recognizing complex disorder mentions in clinical texts. The novel approach significantly improves accuracy, especially for overlapping mentions, advancing clinical information extraction.

Keywords:
Clinical textInformation extractionMulti-labelStructured support vector machine

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Area of Science:

  • Computational linguistics
  • Medical informatics
  • Machine learning

Background:

  • Clinical text information extraction aids faster patient problem identification and future intelligent diagnosis.
  • Recognizing complex and overlapping disorder mentions in clinical narratives remains a challenge.
  • Existing methods struggle with intricate entity recognition tasks.

Purpose of the Study:

  • To propose a novel multi-label structured Support Vector Machine (SVM) for disorder mention recognition.
  • To develop a multi-label scheme capable of handling complicated entity recognition scenarios.
  • To improve the accuracy of identifying overlapping disorder mentions in clinical texts.

Main Methods:

  • A multi-label structured Support Vector Machine (SVM) model was developed.
  • A novel multi-label scheme using 24-bit binary numbers for label representation was designed.
  • The model was evaluated on the 2013 Conference and Labs of the Evaluation Forum dataset.

Main Results:

  • The best F1-Score achieved was 0.7343.
  • The multi-label structured SVM model outperformed the conditional random field (CRF) model.
  • The proposed multi-label scheme showed a 0.1428 higher F1-Score for overlapping mentions compared to the baseline BIOHD1234 scheme.

Conclusions:

  • The multi-label structured SVM approach is effective for disorder recognition tasks.
  • The novel multi-label scheme surpasses baseline performance and enhances recognition of complex mentions.
  • This multi-label scheme is adaptable for various complex entity recognition tasks in different models.