Treatment Planning Validation

Li, Ruijiang; Jiang, Steve B

doi:10.1007/978-3-319-18305-3_14

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…Several pharmaceutical studies [4][5][6][7] have applied ML to find anomalous prescriptions but not tailored to RT. In RT, several studies [8][9][10][11] have used ML to look at the treatment parameters to detect errors in treatment plans, but not focus on prescription error detection.…”

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Wright²,

Hales³

et al. 2021

Preprint

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Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a peer-review process, where the physician's 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 case load. We designed a novel prescription anomaly detection algorithm that utilizes historical data from the past 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 that of 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, 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 specialists. Our model has a lower type II error rate compared to manual peer-review physicians.Our model has many advantages over traditional machine learning algorithms, particularly 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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Li¹,

Wright²,

Hales³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

Wright

Hales

et al. 2022

npj Digit. Med.

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Appropriate dosing of radiation is crucial to patient safety in radiotherapy. Current quality assurance depends heavily on a physician peer-review process, which includes a review of the treatment plan’s dose and fractionation. Potentially, physicians may not identify errors during this manual peer review due to time constraints and caseload. A novel prescription anomaly detection algorithm is designed that utilizes historical data from the past 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 create 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 that of the group with an identical or similar prescription. We flag prescription if either metric is greater than specific optimized cut-off values. We use thoracic cancer patients (n = 2504) as an example and extracted seven features. Our testing set f1 score is between 73%-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 II error rate compared to the manual peer-review by physicians.

show abstract

Treatment Planning Validation

Cited by 2 publications

References 21 publications

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

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

A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

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