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Article

Automatic Incident Triage in Radiation Oncology Incident Learning System

1
Department of Computer Science, Virginia Commonwealth University, Richmond, VA 23284, USA
2
Department of Radiation Oncology, Virginia Commonwealth University, Richmond, VA 23298, USA
3
Department of Veteran Affairs, National Radiation Oncology Program, Richmond, VA 23249, USA
*
Author to whom correspondence should be addressed.
Healthcare 2020, 8(3), 272; https://doi.org/10.3390/healthcare8030272
Received: 2 July 2020 / Revised: 10 August 2020 / Accepted: 11 August 2020 / Published: 14 August 2020
(This article belongs to the Special Issue Feature Papers in Artificial Intelligence in Medicine)
The Radiotherapy Incident Reporting and Analysis System (RIRAS) receives incident reports from Radiation Oncology facilities across the US Veterans Health Affairs (VHA) enterprise and Virginia Commonwealth University (VCU). In this work, we propose a computational pipeline for analysis of radiation oncology incident reports. Our pipeline uses machine learning (ML) and natural language processing (NLP) based methods to predict the severity of the incidents reported in the RIRAS platform using the textual description of the reported incidents. These incidents in RIRAS are reviewed by a radiation oncology subject matter expert (SME), who initially triages some incidents based on the salient elements in the incident report. To automate the triage process, we used the data from the VHA treatment centers and the VCU radiation oncology department. We used NLP combined with traditional ML algorithms, including support vector machine (SVM) with linear kernel, and compared it against the transfer learning approach with the universal language model fine-tuning (ULMFiT) algorithm. In RIRAS, severities are divided into four categories; A, B, C, and D, with A being the most severe to D being the least. In this work, we built models to predict High (A & B) vs. Low (C & D) severity instead of all the four categories. Models were evaluated with macro-averaged precision, recall, and F1-Score. The Traditional ML machine learning (SVM-linear) approach did well on the VHA dataset with 0.78 F1-Score but performed poorly on the VCU dataset with 0.5 F1-Score. The transfer learning approach did well on both datasets with 0.81 F1-Score on VHA dataset and 0.68 F1-Score on the VCU dataset. Overall, our methods show promise in automating the triage and severity determination process from radiotherapy incident reports. View Full-Text
Keywords: incident learning system; deep learning; automated triage; natural language processing; transfer learning incident learning system; deep learning; automated triage; natural language processing; transfer learning
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MDPI and ACS Style

Syed, K.; Sleeman, W., IV; Hagan, M.; Palta, J.; Kapoor, R.; Ghosh, P. Automatic Incident Triage in Radiation Oncology Incident Learning System. Healthcare 2020, 8, 272. https://doi.org/10.3390/healthcare8030272

AMA Style

Syed K, Sleeman W IV, Hagan M, Palta J, Kapoor R, Ghosh P. Automatic Incident Triage in Radiation Oncology Incident Learning System. Healthcare. 2020; 8(3):272. https://doi.org/10.3390/healthcare8030272

Chicago/Turabian Style

Syed, Khajamoinuddin, William Sleeman IV, Michael Hagan, Jatinder Palta, Rishabh Kapoor, and Preetam Ghosh. 2020. "Automatic Incident Triage in Radiation Oncology Incident Learning System" Healthcare 8, no. 3: 272. https://doi.org/10.3390/healthcare8030272

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