Attributes for Causal Inference in Electronic Healthcare Databases

Jenna M Reps,Uwe Aickelin,Jack E Gibson

doi:10.2139/ssrn.2828446

Abstract

Side effects of prescription drugs present a serious issue. Existing algorithms that detect side effects generally require further analysis to confirm causality. In this paper we investigate attributes based on the Bradford-Hill causality criteria that could be used by a classifying algorithm to definitively identify side effects directly. We found that it would be advantageous to use attributes based on the association strength, temporality and specificity criteria.

Highlights

The aim of medication is to improve patients’ standard of living, but medication can lead to side effects, known as adverse drug reactions (ADRs)
In this paper we investigate attributes based on the Bradford Hill causality criteria (BHCC) to aid future ADR classifying algorithms
The attributes chosen by the Correlation-based Feature Selection (CFS) algorithm were LEOPARD, RD13BNF, ABratio Lv3, Gender Ratio and Read Code Level

Summary

Introduction

The aim of medication is to improve patients’ standard of living, but medication can lead to side effects, known as adverse drug reactions (ADRs). Existing ADR signalling algorithms have a high false positive rate. This reduces their efficiency as the signals they generate need to be confirmed with more rigorous analysis. A novel approach for signalling ADRs is to develop a causality classifier with suitable input attributes. Such an algorithm would be more efficient at signalling ADRs as it would not require additional analysis. In this paper we investigate attributes based on the BHCC to aid future ADR classifying algorithms. In the continuation of this paper we summarise the existing algorithms, the BHCC and the feature selection applied, followed by the results and finish with the conclusion

Background & Methodology

Results & Discussion

Conclusion