  Appropriate dosing of radiation is crucial to patient safety in radiotherapy.
Current quality assurance depends heavily on a peer-review process, where the
physicians' peer review on each patient's treatment plan, including dose and
fractionation. However, such a process is manual and laborious. Physicians may
not identify errors due to time constraints and caseload. We designed a novel
prescription anomaly detection algorithm that utilizes historical data to
predict anomalous cases. Such a tool can serve as an electronic peer who will
assist the peer-review process providing extra safety to the patients. In our
primary model, we created two dissimilarity metrics, R and F. R defining how
far a new patient's prescription is from historical prescriptions. F represents
how far away a patient's feature set is from the group with an identical or
similar prescription. We flag prescription if either metric is greater than
specific optimized cut-off values. We used thoracic cancer patients (n=2356) as
an example and extracted seven features. Here, we report our testing f1 score,
between 75%-94% for different treatment technique groups. We also independently
validate our results by conducting a mock peer review with three thoracic
specialists. Our model has a lower type 2 error rate compared to manual
peer-review physicians. Our model has many advantages over traditional machine
learning algorithms, particularly in that it does not suffer from class
imbalance. It can also explain why it flags each case and separate prescription
and non-prescription-related features without learning from the data.
