Detecting adverse drug reactions in the general practice healthcare database. PhD thesis ...

315 Pages·2017·6.01 MB·English

by  Christine Middleton

Detecting Adverse Drug
Reactions in the General
Practice Healthcare Database
Jenna Marie Reps
School of Computer Science
University of Nottingham
A thesis submitted for the degree of
Doctor of Philosophy
2014
Acknowledgements
IwouldliketoacknowledgeandthankmysupervisorsProfessorJonathan
GaribaldiandProfessorUweAickelin, whosguidancemadethisthesis
possible, my family for their continued support during my PhD and
my friends for helping to keep me motivated.
Abstract
The novel contribution of this research is the development of a super-
vised algorithm that extracts relevant attributes from The Health Im-
provement Network database to detect prescription side effects. Pre-
scription drug side effects are a common cause of morbidity through-
out the world. Methods that aim to detect side effects have his-
torically been limited due to the data available, but some of these
limitations may be overcome by incorporating longitudinal observa-
tional databases into pharmacovigilance. Existing side effect detect-
ing methods using longitudinal observational databases have shown
promise at becoming a fundamental component of post marketing
surveillance but unfortunately have high false positive rates. An extra
step is required to further analyse and filter the potential side effects
detected by existing methods due to their high false positive rates,
and this reduces their efficiency. In this thesis a novel methodology,
the supervised adverse drug reaction predictor (SAP) framework, is
presented that learns from known side effects, and identifies patterns
that can be utilised to detect unknown side effects. The Bradford-Hill
causality considerations are used to derive suitable attributes as in-
puts into a learning algorithm. Both supervised and semi-supervised
techniques are investigated due to the limited number of definitively
known side effects. The results showed that the SAP framework im-
plementing a random forest classifier outperformed the existing meth-
ods on The Health Improvement Network longitudinal observational
database, with AUCs ranging between 0.812-0.937, an overall MAP
of 0.667, precision values between 0.733-1 and a false positive rate
≤ 0.013. When applied to the standard reference the SAP frame-
work implementing a support vector machine obtained a MAP score
of 0.490, an average AUC of 0.703 and a false positive rate of 0.16.
Thefalsepositiverateislowerthanthatobtainedbyexistingmethods
on the standard reference.
Contents
Contents iv
List of Figures ix
Nomenclature xv
1 Introduction 1
1.1 Background & Motivation . . . . . . . . . . . . . . . . . . . . . . 2
1.2 Aims & Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . 6
1.2.1 Hypotheses . . . . . . . . . . . . . . . . . . . . . . . . . . 7
1.2.2 Objectives . . . . . . . . . . . . . . . . . . . . . . . . . . . 8
1.3 Thesis Organisation . . . . . . . . . . . . . . . . . . . . . . . . . . 9
1.4 Contribution to Knowledge . . . . . . . . . . . . . . . . . . . . . . 11
2 Literature Review 13
2.1 Current Pharmacovigilance . . . . . . . . . . . . . . . . . . . . . . 13
2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . 13
2.1.2 Causality . . . . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.3 Spontaneous Reporting Databases . . . . . . . . . . . . . . 19
2.1.3.1 Overview . . . . . . . . . . . . . . . . . . . . . . 19
2.1.3.2 Causality . . . . . . . . . . . . . . . . . . . . . . 25
2.1.3.3 Limitations . . . . . . . . . . . . . . . . . . . . . 27
2.1.3.4 Summary . . . . . . . . . . . . . . . . . . . . . . 28
2.1.4 Longitudinal Observational Databases . . . . . . . . . . . 29
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CONTENTS
2.1.4.1 Introduction . . . . . . . . . . . . . . . . . . . . 29
2.1.4.2 Methods . . . . . . . . . . . . . . . . . . . . . . . 33
2.1.4.3 Causality . . . . . . . . . . . . . . . . . . . . . . 42
2.1.4.4 Limitations . . . . . . . . . . . . . . . . . . . . . 43
2.1.4.5 Summary . . . . . . . . . . . . . . . . . . . . . . 43
2.1.5 Combining Multiple Databases . . . . . . . . . . . . . . . 44
2.1.5.1 Overview . . . . . . . . . . . . . . . . . . . . . . 44
2.1.5.2 Summary . . . . . . . . . . . . . . . . . . . . . . 50
2.1.6 Pharmacovigilance Summary . . . . . . . . . . . . . . . . . 50
2.2 Pattern Recognition . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.2.1 Supervised Learning . . . . . . . . . . . . . . . . . . . . . 55
2.2.1.1 Introduction . . . . . . . . . . . . . . . . . . . . 55
2.2.1.2 Classifiers . . . . . . . . . . . . . . . . . . . . . . 61
2.2.1.3 Ensemble Methods . . . . . . . . . . . . . . . . . 69
2.2.1.4 Supervised Learning Summary . . . . . . . . . . 73
2.2.2 Semi-Supervised Learning . . . . . . . . . . . . . . . . . . 74
2.2.2.1 Introduction . . . . . . . . . . . . . . . . . . . . 74
2.2.2.2 Semi-Supervised Clustering . . . . . . . . . . . . 79
2.2.2.3 Metric Learning . . . . . . . . . . . . . . . . . . 80
2.2.2.4 Semi-Supervised Learning Summary . . . . . . . 82
2.2.3 Pattern Recognition Summary . . . . . . . . . . . . . . . . 83
2.3 Literature Review Summary . . . . . . . . . . . . . . . . . . . . . 84
3 Existing Methods Comparison 87
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 88
3.3 Existing Methods . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.3.1 TPD . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 91
3.3.2 MUTARA & HUNT . . . . . . . . . . . . . . . . . . . . . 92
3.3.3 ROR . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.4 Determining Labels . . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.4.1 ADR Labels . . . . . . . . . . . . . . . . . . . . . . . . . . 93
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CONTENTS
3.4.1.1 Online . . . . . . . . . . . . . . . . . . . . . . . . 93
3.4.1.2 SIDER . . . . . . . . . . . . . . . . . . . . . . . 93
3.4.2 Noise Labels . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.5 Measures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 94
3.5.1 Natural Thresholds . . . . . . . . . . . . . . . . . . . . . . 95
3.5.2 Ranking Ability . . . . . . . . . . . . . . . . . . . . . . . . 96
3.6 General Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.6.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 98
3.6.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 99
3.6.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.7 Specific Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.7.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 104
3.7.2 Results and Discussion . . . . . . . . . . . . . . . . . . . . 105
3.8 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 108
4 Incorporating Causation 110
4.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 110
4.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 111
4.2.1 Data Cleansing . . . . . . . . . . . . . . . . . . . . . . . . 113
4.2.2 Data Extraction . . . . . . . . . . . . . . . . . . . . . . . . 113
4.2.2.1 Formulation . . . . . . . . . . . . . . . . . . . . . 113
4.2.2.2 Extraction . . . . . . . . . . . . . . . . . . . . . . 115
4.2.3 Data Derivation . . . . . . . . . . . . . . . . . . . . . . . . 119
4.2.3.1 Association Strength . . . . . . . . . . . . . . . . 121
4.2.3.2 Temporality . . . . . . . . . . . . . . . . . . . . . 123
4.2.3.3 Specificity . . . . . . . . . . . . . . . . . . . . . . 124
4.2.3.4 Biological Gradient . . . . . . . . . . . . . . . . . 126
4.2.3.5 Experimentation . . . . . . . . . . . . . . . . . . 127
4.2.3.6 Other Criteria . . . . . . . . . . . . . . . . . . . 128
4.2.3.7 THIN Specific . . . . . . . . . . . . . . . . . . . . 128
4.2.3.8 A Note on Dependency . . . . . . . . . . . . . . 130
4.2.4 Data Description . . . . . . . . . . . . . . . . . . . . . . . 131
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CONTENTS
4.2.5 Data Transformation . . . . . . . . . . . . . . . . . . . . . 131
4.2.5.1 Continuous Attributes . . . . . . . . . . . . . . . 131
4.2.5.2 Discrete Attributes . . . . . . . . . . . . . . . . . 132
4.2.6 Feature Selection . . . . . . . . . . . . . . . . . . . . . . . 133
4.3 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 135
5 Developing The ADR Learning Framework 136
5.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
5.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 137
5.3 Algorithms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 139
5.3.1 Supervised ADR Predictor . . . . . . . . . . . . . . . . . . 139
5.3.1.1 Training Stage . . . . . . . . . . . . . . . . . . . 141
5.3.1.2 Prediction Stage . . . . . . . . . . . . . . . . . . 142
5.3.1.3 Results and Analysis . . . . . . . . . . . . . . . . 142
5.3.1.4 Summary . . . . . . . . . . . . . . . . . . . . . . 147
5.3.2 Semi-Supervised ADR Predictor . . . . . . . . . . . . . . . 148
5.3.2.1 Self Training Random Forest . . . . . . . . . . . 149
5.3.2.2 Semi-supervised Clustering . . . . . . . . . . . . 151
5.3.2.3 Results and Analysis . . . . . . . . . . . . . . . . 152
5.3.2.4 Summary . . . . . . . . . . . . . . . . . . . . . . 158
5.4 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 158
6 Evaluating The ADR Learning Framework 160
6.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 160
6.2 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 161
6.3 Evaluation using the Standard Reference . . . . . . . . . . . . . . 162
6.3.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
6.3.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 163
6.3.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 164
6.4 Specific Comparison . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.4.1 Method . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
6.4.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
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CONTENTS
6.4.2.1 Nifedipine . . . . . . . . . . . . . . . . . . . . . . 166
6.4.2.2 Ciprofloxacin . . . . . . . . . . . . . . . . . . . . 168
6.4.2.3 Ibuprofen . . . . . . . . . . . . . . . . . . . . . . 170
6.4.2.4 Budesonide . . . . . . . . . . . . . . . . . . . . . 172
6.4.2.5 Naproxen . . . . . . . . . . . . . . . . . . . . . . 173
6.4.3 Discussion . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 181
7 Conclusions 183
7.1 Contributions . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 184
7.2 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 190
7.3 Dissemination . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 193
7.3.1 Journal Papers . . . . . . . . . . . . . . . . . . . . . . . . 193
7.3.2 Conference papers . . . . . . . . . . . . . . . . . . . . . . 194
A The THIN Database 196
B Drugs 205
C Software Details and Preliminary Work 211
C.1 Software Details . . . . . . . . . . . . . . . . . . . . . . . . . . . . 211
C.2 Wrapper Feature Selection . . . . . . . . . . . . . . . . . . . . . . 212
C.3 Preliminary Work . . . . . . . . . . . . . . . . . . . . . . . . . . . 213
D SAP Result Tables 219
References 268
viii
List of Figures
2.1 Illustration of data contained in SRS databases. . . . . . . . . . 20
2.2 AnexampleentityrelationshipdiagramforanSRSdatabasebased
on the FAERS database. . . . . . . . . . . . . . . . . . . . . . . 21
2.3 The online form for submitting suspected ADRs via the Yellow
Card Scheme in the UK. . . . . . . . . . . . . . . . . . . . . . . . 22
2.4 Illustration of patients’ longitudinal data contained in the THIN
database. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
2.5 Illustration of the counterfactual theory of causaltion. . . . . . . . 33
2.6 Illustration of the disproportionality methods. . . . . . . . . . . . 34
2.7 Illustration of the TPD method. . . . . . . . . . . . . . . . . . . 35
2.8 Illustration of the MUTARA and HUNT methods. . . . . . . . . 40
2.9 Illustration of a classifier partitioning the attribute space. Using
the training data (blue dots are labelled as ADR and red as non-
ADR)afunctionistrainedtopartitionthespaceintoADRsections
and non-ADR sections. This can then be used to predict whether
a new data-point is an ADR or non-ADR based on where the data
point lies in the attribute space. . . . . . . . . . . . . . . . . . . . 56
ix