A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization.
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 whethe...
| Published in: | Academic Radiology Vol. 24; no. 2; pp. 160 - 167 |
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| Main Authors: | , |
| Format: | research Journal Article |
| Published: |
Elsevier B.V.
Feb2017
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| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=120654746&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 120654746 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 10766332 T4X jtl: Academic Radiology issn: 10766332 maglogo: N pubinfo: dt: Feb2017 vid: 24 iid: 2 pid: 467 pub: Elsevier B.V. place: New York, New York artinfo: ui: 120654746 120654746 NLM27889399 120654746 10.1016/j.acra.2016.09.013 NLM27889399 120654746 ppf: 160 ppct: 7 formats: tig: atl: A Natural Language Processing-based Model to Automate MRI Brain Protocol Selection and Prioritization. aug: au: Brown, Andrew D. Marotta, Thomas R. affil: Department of Medical Imaging, St Michael's Hospital, Toronto, Ontario, Canada sug: subj: Magnetic Resonance Imaging Methods Brain Diseases Diagnosis Natural Language Processing Algorithms Health and Welfare Planning Magnetic Resonance Imaging Standards Sensitivity and Specificity Protocols Middle Age Retrospective Design Patient Selection Male Human Female Pilot Studies Validation Studies Comparative Studies Evaluation Research Multicenter Studies Middle Aged: 45-64 years Male Female ab: 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.Materials and Methods: To test the feasibility of using an NLP model to support clinical decision making for MRI brain examinations, we designed three different medical imaging prediction tasks, each with a unique outcome: selecting an examination protocol, evaluating the need for contrast administration, and determining priority. We created three models for each prediction task, each using a different classification algorithm-random forest, support vector machine, or k-nearest neighbor-to predict outcomes based on the narrative clinical indications and demographic data associated with 13,982 MRI brain examinations performed from January 1, 2013 to June 30, 2015. Test datasets were used to calculate the accuracy, sensitivity and specificity, predictive values, and the area under the curve.Results: Our optimal results show an accuracy of 82.9%, 83.0%, and 88.2% for the protocol selection, contrast administration, and prioritization tasks, respectively, demonstrating that predictive algorithms can be used to aid in clinical decision support for examination protocoling.Conclusions: NLP models developed from the narrative clinical information provided by referring clinicians and demographic data are feasible methods to predict the protocol and priority of MRI brain examinations. pubtype: Academic Journal doctype: research Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
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