74 citations · 118 across the 15 of their papers we have counts for
18 papers
Using simulation to incorporate dynamic criteria into multiple criteria decision-making
Uwe Aickelin, Jenna Marie Reps, Peer-Olaf Siebers +1
In this paper, we present a case study demonstrating how dynamic and uncertain criteria can be incorporated into a multicriteria analysis with the help of discrete event simulation…
Logistic regression models for patient-level prediction based on massive observational data: Do we need all data?
Luis H. John, Jan A. Kors, Jenna M. Reps +2
Objective: Provide guidance on sample size considerations for developing predictive models by empirically establishing the adequate sample size, which balances the competing object…
Supervised Adverse Drug Reaction Signalling Framework Imitating Bradford Hill's Causality Considerations
Jenna Marie Reps, Jonathan M. Garibaldi, Uwe Aickelin +2
Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunat…
Refining adverse drug reaction signals by incorporating interaction variables identified using emergent pattern mining
Jenna M. Reps, Uwe Aickelin, Richard B. Hubbard
Purpose: To develop a framework for identifying and incorporating candidate confounding interaction terms into a regularised cox regression analysis to refine adverse drug reaction…
Identifying Candidate Risk Factors for Prescription Drug Side Effects using Causal Contrast Set Mining
Jenna Reps, Zhaoyang Guo, Haoyue Zhu +1
Big longitudinal observational databases present the opportunity to extract new knowledge in a cost effective manner. Unfortunately, the ability of these databases to be used for c…
Personalising Mobile Advertising Based on Users Installed Apps
Jenna Reps, Uwe Aickelin, Jonathan Garibaldi +1
Mobile advertising is a billion pound industry that is rapidly expanding. The success of an advert is measured based on how users interact with it. In this paper we investigate whe…
Refining Adverse Drug Reactions using Association Rule Mining for Electronic Healthcare Data
Jenna M. Reps, Uwe Aickelin, Jiangang Ma +1
Side effects of prescribed medications are a common occurrence. Electronic healthcare databases present the opportunity to identify new side effects efficiently but currently the m…
Incorporating Spontaneous Reporting System Data to Aid Causal Inference in Longitudinal Healthcare Data
Jenna Reps, Uwe Aickelin
Inferring causality using longitudinal observational databases is challenging due to the passive way the data are collected. The majority of associations found within longitudinal…
Attributes for Causal Inference in Longitudinal Observational Databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The pharmaceutical industry is plagued by the problem of side effects that can occur anytime a prescribed medication is ingested. There has been a recent interest in using the vast…
Tuning a Multiple Classifier System for Side Effect Discovery using Genetic Algorithms
Jenna M. Reps, Uwe Aickelin, Jonathan M. Garibaldi
In previous work, a novel supervised framework implementing a binary classifier was presented that obtained excellent results for side effect discovery. Interestingly, unique side…
Signalling Paediatric Side Effects using an Ensemble of Simple Study Designs
Jenna M. Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Background: Children are frequently prescribed medication off-label, meaning there has not been sufficient testing of the medication to determine its safety or effectiveness. The m…
A Novel Semi-Supervised Algorithm for Rare Prescription Side Effect Discovery
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Drugs are frequently prescribed to patients with the aim of improving each patient's medical state, but an unfortunate consequence of most prescription drugs is the occurrence of u…
Comparing Stochastic Differential Equations and Agent-Based Modelling and Simulation for Early-stage Cancer
Grazziela P Figueredo, Peer-Olaf Siebers, Markus R Owen +2
There is great potential to be explored regarding the use of agent-based modelling and simulation as an alternative paradigm to investigate early-stage cancer interactions with the…
Comparison of algorithms that detect drug side effects using electronic healthcare databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The electronic healthcare databases are starting to become more readily available and are thought to have excellent potential for generating adverse drug reaction signals. The Heal…
Comparing Data-mining Algorithms Developed for Longitudinal Observational Databases
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
Longitudinal observational databases have become a recent interest in the post marketing drug surveillance community due to their ability of presenting a new perspective for detect…
Discovering Sequential Patterns in a UK General Practice Database
Jenna Reps, Jonathan M. Garibaldi, Uwe Aickelin +3
The wealth of computerised medical information becoming readily available presents the opportunity to examine patterns of illnesses, therapies and responses. These patterns may be…
Quiet in Class: Classification, Noise and the Dendritic Cell Algorithm
Feng Gu, Jan Feyereisl, Robert Oates +3
Theoretical analyses of the Dendritic Cell Algorithm (DCA) have yielded several criticisms about its underlying structure and operation. As a result, several alterations and fixes…
Investigating the Detection of Adverse Drug Events in a UK General Practice Electronic Health-Care Database
Jenna Reps, Jan Feyereisl, Jonathan M. Garibaldi +3
Data-mining techniques have frequently been developed for Spontaneous reporting databases. These techniques aim to find adverse drug events accurately and efficiently. Spontaneous…