Using Rule-Based Decision Trees to Digitize Legislation

Henry R F Mingay,Aaron Ceross,Rita Hendricusdottir,Jeroen H M Bergmann

doi:10.3390/prosthesis4010012

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.

Highlights

Regulations contain rules setup by authorities to control specific aspects of certain industries, which often influences the way companies operate
The In Vitro Diagnostic Regulation (IVDR) decision tree was tested using 55 example in vitro diagnostic (IVD) devices with known classifications according to the IVDR
The decision tree was shown to accurately classify all IVD devices where there was no ambiguity in interpretation of the rules in the IVDR

Summary

Introduction

Regulations contain rules setup by (governmental) authorities to control specific aspects of certain industries, which often influences the way companies operate. There are two types of classification problems resulting from legislation: identification of relevant regulation, and classification of certain use cases for conformity requirements within the legislation. While all products and services need to consider relevant legislation, a smaller subset of legislations contain risk-based classification problems within the legislative document. Examples of this include the classification of data types within data protection regulation It is not dependent on a specific training dataset This use case will focus on the classification problem within the IVDR legislation, it is applicable for determining whether the devices are governed by the IVDR. Stakeholders require greater understanding of these types of legislation as the conformity requirements are dependent on the risk level of their product or service

Digitizing Legislation

Decision Trees for Digitizing Legislation

IVDR Legislation as a Case Study Example of RBDT-1C

Building the RBDT-1C

Classification Results from the IVDR Decision Tree, Build Using the RBDT-1C Algorithm

Discussion