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Benchmarking clinical speech recognition and information extraction: new data, methods, and evaluations.

Hanna Suominen1, Liyuan Zhou, Leif Hanlen

  • 1Canberra Research Laboratory, Machine Learning Research Group, NICTA, Canberra, ACT, Australia. hanna.suominen@nicta.com.au.

JMIR Medical Informatics
|April 29, 2015
PubMed
Summary

This study introduces a novel method using speech recognition and information extraction to improve clinical handover by creating accurate, data-rich documentation. The open dataset supports research in natural language processing for healthcare.

Keywords:
computer systems evaluationdata collectioninformation extractionnursing recordspatient handoffrecords as topicspeech recognition software

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Area of Science:

  • Medical Informatics
  • Natural Language Processing
  • Health Informatics

Background:

  • Failures in clinical information flow contribute to preventable adverse events.
  • Manual handover notes result in significant information loss over shifts.
  • Speech recognition and information extraction offer a solution for automated clinical documentation.

Purpose of the Study:

  • To create a dataset of recorded spoken handovers with annotated transcriptions for natural language processing research.
  • To develop and evaluate a system for automated clinical handover form completion.
  • To provide a web application demonstrating the system's workflow and design.

Main Methods:

  • Utilized Dragon Medical 11.0 for speech recognition and CRF++ for information extraction.
  • Employed CoreNLP, MetaMap, and Ontoserver for feature computation in information extraction.
  • Applied cross-validation techniques for evaluating processing accuracy.

Main Results:

  • Achieved 5276/7277 word recognition accuracy in speech-to-text conversion.
  • Obtained an F1 score of 0.86 for irrelevant text and a macro-averaged F1 of 0.70 for 35 categories in information extraction.
  • Evaluated on 100-101 test documents derived from simulated nursing handovers.

Conclusions:

  • The study releases an open dataset, benchmarks, and software to advance clinical documentation and language processing research.
  • This data supports research in speech-to-text and information extraction for healthcare applications.
  • The dataset is utilized in the CLEFeHealth 2015 evaluation for speech recognition tasks.