Automatic Classification of Critical Findings in Radiology Reports

Aditya Tiwari, Samah Fodeh, Steven Baccei, Max Rosen
Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining, PMLR 69:35-39, 2017.

Abstract

Communication of “actionable” findings in radiology reports is an important part of high quality medical care. Distinguishing radiology reports with “actionable” findings from other reports is currently a function of the radiologist and largely a manual process. This paper describes a system for automatic classification of patient’s radiology reports as it relates to the degree of severity of “actionable” findings provided by the radiology department at University of Massachusetts Medical School. This is done by using machine learning classifier on text based features. Several machine learning classification algorithms are evaluated and compared. Random forest classifier performed the best in this case while other classification methods also performed decently.

Cite this Paper

BibTeX
@InProceedings{pmlr-v69-tiwari17a, title = {Automatic Classification of Critical Findings in Radiology Reports}, author = {Tiwari, Aditya and Fodeh, Samah and Baccei, Steven and Rosen, Max}, booktitle = {Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining}, pages = {35--39}, year = {2017}, editor = {Fodeh, Samah and Raicu, Daniela Stan}, volume = {69}, series = {Proceedings of Machine Learning Research}, month = {14 Aug}, publisher = {PMLR}, pdf = {http://proceedings.mlr.press/v69/tiwari17a/tiwari17a.pdf}, url = {https://proceedings.mlr.press/v69/tiwari17a.html}, abstract = {Communication of “actionable” findings in radiology reports is an important part of high quality medical care. Distinguishing radiology reports with “actionable” findings from other reports is currently a function of the radiologist and largely a manual process. This paper describes a system for automatic classification of patient’s radiology reports as it relates to the degree of severity of “actionable” findings provided by the radiology department at University of Massachusetts Medical School. This is done by using machine learning classifier on text based features. Several machine learning classification algorithms are evaluated and compared. Random forest classifier performed the best in this case while other classification methods also performed decently.} }
Endnote
%0 Conference Paper %T Automatic Classification of Critical Findings in Radiology Reports %A Aditya Tiwari %A Samah Fodeh %A Steven Baccei %A Max Rosen %B Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining %C Proceedings of Machine Learning Research %D 2017 %E Samah Fodeh %E Daniela Stan Raicu %F pmlr-v69-tiwari17a %I PMLR %P 35--39 %U https://proceedings.mlr.press/v69/tiwari17a.html %V 69 %X Communication of “actionable” findings in radiology reports is an important part of high quality medical care. Distinguishing radiology reports with “actionable” findings from other reports is currently a function of the radiologist and largely a manual process. This paper describes a system for automatic classification of patient’s radiology reports as it relates to the degree of severity of “actionable” findings provided by the radiology department at University of Massachusetts Medical School. This is done by using machine learning classifier on text based features. Several machine learning classification algorithms are evaluated and compared. Random forest classifier performed the best in this case while other classification methods also performed decently.
APA
Tiwari, A., Fodeh, S., Baccei, S. & Rosen, M.. (2017). Automatic Classification of Critical Findings in Radiology Reports. Proceedings of The First Workshop Medical Informatics and Healthcare held with the 23rd SIGKDD Conference on Knowledge Discovery and Data Mining, in Proceedings of Machine Learning Research 69:35-39 Available from https://proceedings.mlr.press/v69/tiwari17a.html.

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