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Marta B Fernandes1,2,3, Navid Valizadeh1,2, Haitham S Alabsi1,2

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A new natural language processing (NLP) algorithm accurately extracts neurologic outcomes from electronic health records (EHR), enabling larger scale studies. This automated method analyzes clinical notes to determine patient recovery levels, improving research efficiency.

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CoronavirusGlasgow outcome scaleIntensive care unitMachine learningModified Rankin ScaleNatural language processing

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Area of Science:

  • Medical Informatics
  • Computational Linguistics
  • Neurology

Background:

  • Assessing neurologic disability at hospital discharge is crucial for clinical research.
  • Manual review of electronic health records (EHR) for neurologic outcomes is time-consuming and labor-intensive.
  • Existing methods limit the scale of neurological outcomes research.

Purpose of the Study:

  • To develop and validate a natural language processing (NLP) algorithm for automated extraction of neurologic outcomes from clinical notes.
  • To enable larger-scale studies on neurological outcomes using EHR data.
  • To improve the efficiency and accuracy of neurologic outcome assessment.

Main Methods:

  • Collected 7314 clinical notes (discharge summaries, PT/OT notes) from 3632 patients.
  • Clinical experts assigned scores using the Glasgow Outcome Scale (GOS) and Modified Rankin Scale (mRS).
  • Trained a multiclass logistic regression model with LASSO regularization and cross-validation on preprocessed note features.

Main Results:

  • The NLP model achieved high performance: GOS micro-average AUC of 0.94 and F-score of 0.77; mRS micro-average AUC of 0.90 and F-score of 0.59.
  • The model demonstrated accurate assignment of neurologic outcomes from free-text clinical notes.
  • Interrater reliability was established for GOS and mRS scoring by experts.

Conclusions:

  • An NLP algorithm can accurately determine neurologic outcomes from clinical notes.
  • This automated approach significantly enhances the feasibility of large-scale neurological outcomes research using EHR data.
  • The developed NLP tool offers a scalable solution for neurologic outcome assessment in clinical research.