Semi-automated text mining strategies for identifying rare causes of injuries from emergency room triage data-Reference-Cited by-同舟云学术

Semi-automated text mining strategies for identifying rare causes of injuries from emergency room triage data

Published:2019-03-25 Issue:2 Volume:9 Page:157-171
ISSN:2472-5579
Container-title:IISE Transactions on Healthcare Systems Engineering
language:en
Short-container-title:IISE Transactions on Healthcare Systems Engineering

Author:

Nanda Gaurav¹,Vallmuur Kirsten²³,Lehto Mark⁴

Affiliation:

1. School of Engineering Education, School of Industrial Engineering, Purdue University, West Lafayette, IN, USA;

2. Institute of Health and Biomedical Innovation and School of Public Health and Social Work, Queensland University of Technology, Brisbane, Australia;

3. Jamieson Trauma Institute, Royal Brisbane and Women's Hospital, Metro North Hospital and Health Service, Queensland Health;

4. School of Industrial Engineering, Purdue University, West Lafayette, IN, USA

Publisher

Informa UK Limited

Subject

Public Health, Environmental and Occupational Health,Safety Research,Safety, Risk, Reliability and Quality

Link

https://www.tandfonline.com/doi/pdf/10.1080/24725579.2019.1567628

Reference29 articles.

1. Aggarwal, C. C., and Zhai, C. (2012) An introduction to text mining. Pp. 1–10 in Mining Text Data, C. C. Aggarwal and C. Zhai (Eds.). Retrieved from http://link.springer.com/chapter/10.1007/978-1-4614-3223-4_1

2. A study of the behavior of several methods for balancing machine learning training data

3. Development and evaluation of a Naïve Bayesian model for coding causation of workers’ compensation claims

4. Comparison of methods for auto-coding causation of injury narratives

5. Editorial

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