Next Article in Journal
Gait Performance of Friction-Based Prosthetic Knee Joint Swing-Phase Controllers in Under-Resourced Settings
Next Article in Special Issue
The Emerging Field of Medical Regulatory Technology and Data Science
Previous Article in Journal
On the Modeling of Transcatheter Therapies for the Aortic and Mitral Valves: A Review
Previous Article in Special Issue
Regulating Environmental Impact of Medical Devices in the United Kingdom—A Scoping Review
 
 
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Using Rule-Based Decision Trees to Digitize Legislation

by
Henry R. F. Mingay
,
Rita Hendricusdottir
,
Aaron Ceross
and
Jeroen H. M. Bergmann
*
Department of Engineering Science, University of Oxford, Parks Road, Oxford OX1 3PJ, UK
*
Author to whom correspondence should be addressed.
Prosthesis 2022, 4(1), 113-124; https://doi.org/10.3390/prosthesis4010012
Submission received: 8 January 2022 / Revised: 9 February 2022 / Accepted: 1 March 2022 / Published: 10 March 2022
(This article belongs to the Special Issue Regulatory Data Science for Medical Devices)

Abstract

This article introduces a novel approach to digitize legislation using rule based-decision trees (RBDTs). As regulation is one of the major barriers to innovation, novel methods for helping stakeholders better understand, and conform to, legislation are becoming increasingly important. Newly introduced medical device regulation has resulted in an increased complexity of regulatory strategy for manufacturers, and the pressure on notified body resources to support this process is making this an increasing concern in industry. This paper explores a real-world classification problem that arises for medical device manufacturers when they want to be certified according to the In Vitro Diagnostic Regulation (IVDR). A modification to an existing RBDT algorithm is introduced (RBDT-1C) and a case study demonstrates how this method can be applied. The RBDT-1C algorithm is used to design a decision tree to classify IVD devices according to their risk-based classes: Class A, Class B, Class C and Class D. The applied RBDT-1C algorithm demonstrated accurate classification in-line with published ground-truth data. This approach should enable users to better understand the legislation, has informed policy makers about potential areas for future guidance, and allowed for the identification of errors in the regulations that have already been recognized and amended by the European Commission.
Keywords: classification; healthcare; innovation; regulation; medical devices; decision tree complexity classification; healthcare; innovation; regulation; medical devices; decision tree complexity

Share and Cite

MDPI and ACS Style

Mingay, H.R.F.; Hendricusdottir, R.; Ceross, A.; Bergmann, J.H.M. Using Rule-Based Decision Trees to Digitize Legislation. Prosthesis 2022, 4, 113-124. https://doi.org/10.3390/prosthesis4010012

AMA Style

Mingay HRF, Hendricusdottir R, Ceross A, Bergmann JHM. Using Rule-Based Decision Trees to Digitize Legislation. Prosthesis. 2022; 4(1):113-124. https://doi.org/10.3390/prosthesis4010012

Chicago/Turabian Style

Mingay, Henry R. F., Rita Hendricusdottir, Aaron Ceross, and Jeroen H. M. Bergmann. 2022. "Using Rule-Based Decision Trees to Digitize Legislation" Prosthesis 4, no. 1: 113-124. https://doi.org/10.3390/prosthesis4010012

APA Style

Mingay, H. R. F., Hendricusdottir, R., Ceross, A., & Bergmann, J. H. M. (2022). Using Rule-Based Decision Trees to Digitize Legislation. Prosthesis, 4(1), 113-124. https://doi.org/10.3390/prosthesis4010012

Article Metrics

Back to TopTop