A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

Journal: Academic radiology

PMID: 27889399

Abstract

RATIONALE AND OBJECTIVES: Incorrect imaging protocol selection can contribute to increased healthcare cost and waste. To help healthcare providers improve the quality and safety of medical imaging services, we developed and evaluated three natural language processing (NLP) models to determine whether NLP techniques could be employed to aid in clinical decision support for protocoling and prioritization of magnetic resonance imaging (MRI) brain examinations.

Authors

Andrew D Brown

Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada. Electronic address: andrew.brown@sloan.mit.edu.
Thomas R Marotta

Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada.

Keywords

Algorithms Brain Diseases Clinical Protocols Feasibility Studies Female Health Priorities Humans Magnetic Resonance Imaging Male Middle Aged Natural Language Processing Patient Selection Retrospective Studies Sensitivity and Specificity Support Vector Machine

External Resources

View on PubMed Access via DOI PubMed (27889399)

A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.

Abstract

Authors

Keywords

External Resources

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