Bulletin of the American Physical Society
APS March Meeting 2022
Volume 67, Number 3
Monday–Friday, March 14–18, 2022; Chicago
Session Q29: Artificial Intelligence, Machine Learning, and Quantitative Biomarkers in Medicine and Biomedicine
3:00 PM–6:00 PM,
Wednesday, March 16, 2022
Room: McCormick Place W-190B
Chair: MIchael Boss, American College of Radiology
Abstract: Q29.00011 : Automatic Prescription Anomaly Detection Tool Assisting Peer Review Chart Rounds in Radiotherapy*
5:24 PM–5:36 PM
Presenter:
Qiongge Li
(Johns Hopkins University School of Medic)
Authors:
Qiongge Li
(Johns Hopkins University School of Medic)
Jean Wright
(Johns Hopkins University Hospital)
Russell Hales
(Johns Hopkins University Hospital)
Todd McNutt
(Johns Hopkins University Hospital)
Ranh Voong
(Johns Hopkins University Hospital)
In our primary model, we created two dissimilarity metrics, “R” and “F”. R defines how far a new patient’s prescription is from the historical patients’ prescriptions. F represents how far away a patient’s feature set is away from that of the group who has an identical or similar prescription. We flag the patients if either metric is greater than certain optimized cut-off values. We used thoracic cancer patients (n=3125) as an example and extracted seven features. Here, we report our testing F1 score which is between 73%-94% for different treatment technique groups.
We also independently validate our results by conducting a mock peer review with three thoracic specialty MDs. Our model has the lowest miss rate (false negative rate) and outperformed MDs in the recall, precision, F1, and accuracy scores.
Our model has many advantages over traditional ML algorithms, such as that it does not suffer from the class-imbalance problem. It can also explain why it flags each case and can impose the separation between prescription features and non-prescription-related features without having to be learned from the data.
*NSF (grant number: 2035750)
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