A novel data-driven algorithm to predict anomalous prescription based on patient's feature set

• URL: http://arxiv.org/abs/2111.15101v1
• Date: Tue, 30 Nov 2021 03:40:24 GMT
• Title: A novel data-driven algorithm to predict anomalous prescription based on patient's feature set
• Authors: Qiongge Li, Jean Wright, Russell Hales, Ranh Voong and Todd McNutt
• Abstract summary: Current quality assurance depends heavily on a peer-review process, where the physicians' peer review on each patient's treatment plan. We designed a novel prescription anomaly detection algorithm that utilizes historical data to predict anomalous cases. Our model has a lower type 2 error rate compared to manual peer-review physicians.
• Score: 0.0
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: 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.

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