A Multiview Model for Detecting the Inappropriate Use of Prescription Medication: Machine Learning Approach

Zhuo, Lin; Cheng, Yinchu; Liu, Shaoqin; Yu, Yang; Tang, Shuang; Jian-cun, Zhen; Zhao, Junfeng; Zhan, Siyan

doi:10.2196/16312

Cited by 3 publications

(1 citation statement)

References 61 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There is an increasing trend to study how machine learning (ML) tools can be used to augment medical professionals' decisions concerning diagnosis, treatment safety, and quality of patient care [2,3]. 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

View full text Add to dashboard Cite

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

Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review

Ranchon

Chanoine

Lambert-Lacroix

et al. 2023

International Journal of Medical Informatics

View full text Add to dashboard Cite

A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

Wright

Hales

et al. 2022

npj Digit. Med.

View full text Add to dashboard Cite

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

A Multiview Model for Detecting the Inappropriate Use of Prescription Medication: Machine Learning Approach

Cited by 3 publications

References 61 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

Development of artificial intelligence powered apps and tools for clinical pharmacy services: A systematic review

A digital physician peer to automatically detect erroneous prescriptions in radiotherapy

Contact Info

Product

Resources

About