A supervised adverse drug reaction signalling framework imitating Bradford Hill’s causality considerations

Big longitudinal observational medical data potentially hold a wealth of information and have been recognised as potential sources for gaining new drug safety knowledge. Unfortunately there are many complexities and underlying issues when analysing longitudinal observational data. Due to these complexities, existing methods for large-scale detection of negative side effects using observational data all tend to have issues distinguishing between association and causality. New methods that can better discriminate causal and non-causal relationships need to be developed to fully utilise the data. In this paper we propose using a set of causality considerations developed by the epidemiologist Bradford Hill as a basis for engineering features that enable the application of supervised learning for the problem of detecting negative side effects. The Bradford Hill considerations look at various perspectives of a drug and outcome relationship to determine whether it shows causal traits. We taught a classifier to find patterns within these perspectives and it learned to discriminate between association and causality. The novelty of this research is the combination of supervised learning and Bradford Hill’s causality considerations to automate the Bradford Hill’s causality assessment. We evaluated the framework on a drug safety gold standard known as the observational medical outcomes partnership’s non-specified association reference set. The methodology obtained excellent discrimination ability with area under the curves ranging between 0.792 and 0.940 (existing method optimal: 0.73) and a mean average precision of 0.640 (existing method optimal: 0.141). The proposed features can be calculated efficiently and be readily updated, making the framework suitable for big observational data

Detecting adverse drug reactions in the general practice healthcare database

The novel contribution of this research is the development of a supervised algorithm that extracts relevant attributes from The Health Improvement Network database to detect prescription side effects. Prescription drug side effects are a common cause of morbidity throughout the world. Methods that aim to detect side effects have historically been limited due to the data available, but some of these limitations may be overcome by incorporating longitudinal observational databases into pharmacovigilance. Existing side effect detecting 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 inputs 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 implementing a random forest classifier outperformed the existing methods 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 framework 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. The false positive rate is lower than that obtained by existing methods on the standard reference

Using the Literature to Identify Confounders

Prior work in causal modeling has focused primarily on learning graph structures and parameters to model data generating processes from observational or experimental data, while the focus of the literature-based discovery paradigm was to identify novel therapeutic hypotheses in publicly available knowledge. The critical contribution of this dissertation is to refashion the literature-based discovery paradigm as a means to populate causal models with relevant covariates to abet causal inference. In particular, this dissertation describes a generalizable framework for mapping from causal propositions in the literature to subgraphs populated by instantiated variables that reflect observational data. The observational data are those derived from electronic health records. The purpose of causal inference is to detect adverse drug event signals. The Principle of the Common Cause is exploited as a heuristic for a defeasible practical logic. The fundamental intuition is that improbable co-occurrences can be “explained away” with reference to a common cause, or confounder. Semantic constraints in literature-based discovery can be leveraged to identify such covariates. Further, the asymmetric semantic constraints of causal propositions map directly to the topology of causal graphs as directed edges. The hypothesis is that causal models conditioned on sets of such covariates will improve upon the performance of purely statistical techniques for detecting adverse drug event signals. By improving upon previous work in purely EHR-based pharmacovigilance, these results establish the utility of this scalable approach to automated causal inference

PTSD and Substance Use Disorders among Offenders: Examining the Effects of TBI, Gender and Interpersonal Violence Victimization

Offender populations have high rates of substance use disorders (SUDs) as well as violence, traumatic brain injury (TBI), and post traumatic stress disorder (PTSD). The lack of screening and treatment of co-occurring disorders has been cited as a major barrier to treating SUDs in offenders. A significant proportion of the offender population has at least one co-occurring disorder with their substance use. Often co-occurring disorders are related to SUDs. Evidence suggests that interpersonal violence victimization (IPVV), TBI, and PTSD are related to SUD and that PTSD alone may also contribute to criminality. The specific aims of this research are to: 1. Determine factors associated with PTSD. 2. Determine if gender differences exist in the relationship between IPVV and SUDs. 3. Determine whether there are differences by TBI status in the relationship between IPVV and SUD. 4. Determine factors that mediate the relationship between gender and long-term illicit hard drug use (HDU) and also between gender and illicit HDU severity. This research study used a gender-stratified random sample from the Statewide Investigation of Traumatic Brain Injury Among Prisoners (SITBIP) study and follows a cross-sectional study design. Three hundred twenty male and 316 female offenders housed in South Carolina state prisons were interviewed from April 2009-April 2010. We found that rates of lifetime and current PTSD exceeded the rates found in the general population, with females having over twice the prevalence as males. Overall, trauma, psychiatric disorders, alcohol and drug use, poorer health, increased impulsivity, TBI, and lower resiliency scores were associated with lifetime PTSD. Controlling for covariates, a 47% difference was detected in the magnitude of the association between IPVV and SUD, by TBI status. No differences were found in the IPVV-SUD relationship by gender when controlling for covariates. Finally, the relationship between female gender and long-term illicit HDU and illicit HDU severity was found to be partially attributable to direct violence

Detecting adverse drug reactions in the general practice healthcare database

The novel contribution of this research is the development of a supervised algorithm that extracts relevant attributes from The Health Improvement Network database to detect prescription side effects. Prescription drug side effects are a common cause of morbidity throughout the world. Methods that aim to detect side effects have historically been limited due to the data available, but some of these limitations may be overcome by incorporating longitudinal observational databases into pharmacovigilance. Existing side effect detecting 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 inputs 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 implementing a random forest classifier outperformed the existing methods 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 framework 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. The false positive rate is lower than that obtained by existing methods on the standard reference

The relationships between the antecedents of childhood maltreatment and adult borderline personality disorder

El primer estudio examinó las propiedades psicométricas de la versión española del Childhood Trauma Questionnaire-Short Form (CTQ-SF) en una muestra clínica de mujeres (n=185). Los resultados mostraron una adecuada fiabilidad de consistencia interna y un buen ajuste de la estructura factorial al modelo de cinco factores. Las escalas de cuidado del PBI correlacionaron negativamente con las escalas del CTQ-SF, y las escalas de sobreprotección del PBI positivamente. El segundo estudio examinó la relación de distintos tipos de maltrato, el estilo educativo parental y los criterios del Trastorno Límite de la Personalidad (TLP), controlando el efecto simultáneo de las experiencias infantiles adversas y los síntomas del Eje I y II en una muestra clínica de 109 mujeres. Los resultados apoyaron la asociación entre el abuso emocional y sexual y los criterios TLP. Los resultados no apoyaron la relación entre el estilo parental y los criterios de TLP.The first study examined the psychometrics properties of the Spanish version of the Childhood Trauma Questionnaire-Short Form (CTQ-SF) in a clinical sample of females (n=185). The results revealed adequate internal consistency reliability of the Spanish CTQ-SF and a good fit of the factor structure to the original version’s five-factor model. The caring scale from the PBI was negatively correlated with CTQ-SF scales, and the PBI overprotection scale was positively correlated with the CTQ-SF scales. The second study examined the relationship of different types of childhood maltreatment and perceived parenting style with Borderline Personality Disorder (BPD) criteria, controlling for the effect of simultaneous adverse experiences and Axis I and II symptoms in a sample of 109 female patients. The results supported an association between emotional and sexual abuse and BPD criteria. The results did not support a relationship between parenting style and BPD criteria