Adverse drug reaction extraction on electronic health records written in Spanish

148 p.This work focuses on the automatic extraction of Adverse Drug Reactions (ADRs) in Electronic HealthRecords (EHRs). That is, extracting a response to a medicine which is noxious and unintended and whichoccurs at doses normally used. From Natural Language Processing (NLP) perspective, this wasapproached as a relation extraction task in which the drug is the causative agent of a disease, sign orsymptom, that is, the adverse reaction.ADR extraction from EHRs involves major challenges. First, ADRs are rare events. That is, relationsbetween drugs and diseases found in an EHR are seldom ADRs (are often unrelated or, instead, related astreatment). This implies the inference from samples with skewed class distribution. Second, EHRs arewritten by experts often under time pressure, employing both rich medical jargon together with colloquialexpressions (not always grammatical) and it is not infrequent to find misspells and both standard andnon-standard abbreviations. All this leads to a high lexical variability.We explored several ADR detection algorithms and representations to characterize the ADR candidates.In addition, we have assessed the tolerance of the ADR detection model to external noise such as theincorrect detection of implied medical entities implied in the ADR extraction, i.e. drugs and diseases. Westtled the first steps on ADR extraction in Spanish using a corpus of real EHRs

Text Classification With Deep Neural Networks

The thesis explores different extensions of Deep Neural Networks in learning underlying natural language representations and how to apply them in Natural Language Processing tasks. Novel methods of learning lower or higher level features of natural languages are given in which word and phrase dense representations are derived from unlabelled corpora. Word representations are learned by training Deep Neural Networks to predict context from each sentence while phrase representations are learned by unsupervised learning with Convolutional Restricted Boltzmann Machine. It is shown that word representations learned from architectures which preserve text input as sequences have better word similarity and relatedness than bag-of-word approaches. Additionally phrase representations learned with Convolutional Restricted Boltzmann Machine when combined with bag-of-word features improve results of text classification tasks over only bag-of-word features. Beside learning word and phrase representations, to the best of my knowledge, the work in the thesis is first to explore Deep Neural Networks in Adverse Drug Reaction detection task where my architectures when used with pre-trained word representations significantly outperform the state-of-the-art models. In addition, outputs from my proposed attentional architecture can be used to highlight important word spans without explicit training labels. In the future I propose the learned representations to be used with the discussed Deep Neural Networks in different NLP tasks such as Dialog Systems, Machine Translation or Natural Language Inference

Developing novel non-invasive MRI techniques to assess cerebrospinal fluid-interstitial fluid (CSF-ISF) exchange

The pathological cascade of events in Alzheimer’s disease (AD) is initiated decades prior to the onset of symptoms. Despite intensive research, the relative time-course/interaction of these events is yet to be determined. Recent evidence suggests that impairments to brain clearance (facilitated by the compartmental exchange of cerebrospinal-fluid (CSF) with interstitial-fluid (ISF)), contributes to the build-up of amyloid and tau (AD hallmarks). Therefore, abnormalities in CSF-ISF exchange dynamics, may represent an early driver of downstream events. Clinical evaluation of this hypothesis is hampered due to the lack of non-invasive CSF-ISF exchange assessment techniques. In this thesis, the primary aim was to develop a physiologically relevant, non-invasive CSF-ISF exchange assessment technique that would circumvent the limitations associated with current procedures (primarily their invasiveness). Towards this goal, animal studies were conducted to investigate the feasibility of a contrast enhanced-magnetic resonance imaging (CE-MRI) approach as a potential non-invasive CSF-ISF exchange imaging technique. Another aim of this thesis was to investigate whether the proposed MRI platform could detect abnormalities in CSF-ISF exchange, a condition hypothesised to occur in the early stages of AD. As such, pharmacological intervention studies were conducted to alter CSF-ISF exchange dynamics. CE-MRI, in conjunction with high-level image post-processing, demonstrated high sensitivity to physiological CSF-ISF exchange. This novel, non-invasive platform, captured dynamic, whole-brain infiltration of contrast agent from the blood to the CSF and into the parenchyma, via a pathway named ‘VEntricular-Cerebral TranspORt (VECTOR)’. Additionally, the platform detected significant abnormalities in CSF-ISF exchange following pharmacological intervention, demonstrating the potential of VECTOR in the study of the parenchymal accumulation of aberrant proteins. Development of this platform is a breakthrough step towards the clinical assessment of CSF-ISF exchange abnormalities to study its role in disease onset/progression, an approach that may inform understanding of the causal sequence of pathological events that occurs in AD development

Current Issues and Recent Advances in Pacemaker Therapy

Patients with implanted pacemakers or defibrillators are frequently encountered in various healthcare settings. As these devices may be responsible for, or contribute to a variety of clinically significant issues, familiarity with their function and potential complications facilitates patient management. This book reviews several clinically relevant issues and recent advances of pacemaker therapy: implantation, device follow-up and management of complications. Innovations and research on the frontiers of this technology are also discussed as they may have wider utilization in the future. The book should provide useful information for clinicians involved in the management of patients with implanted antiarrhythmia devices and researchers working in the field of cardiac implants

Performance-Based Quality Specifications: The Link between Product Development and Clinical Outcomes

The design of drug delivery systems and their corresponding dosing guidelines are critical product development functions supported by clinical pharmacokinetic (PK) and pharmacodynamic (PD) data. Largely, the importance of variance and covariance in product and patient attributes is poorly understood. The existence of PK/PD diversity among myriad patient sub-populations further complicates efforts to gauge the importance of product quality variation. Nevertheless, a platform capable of evaluating the effects of product and patient variability on clinical performance was constructed. This dissertation was predicated on requests to re-define pharmaceutical quality in terms of risk by relating clinical attributes to production characteristics. To avoid in vivo studies, simulated experimental trials were conducted using the model drug, theophylline, for which data and models could be acquired from the literature. Where comprehensive data were unavailable (e.g., production variability statistics), initial estimates were acquired via laboratory-scale experiments. Model asthmatic patients were generated using Monte Carlo simulation and published population distributions of various anothropometric measurements, disease rates, and lifestyle factors. Mathematical constructs for in vitro-in vivo correlations provide a linkage between Quality by Design (QbD) product and process models, PK/PD models, and patient population statistics. The combined models formed the foundation for Monte Carlo risk assessments, which characterized the risk of inefficacy and toxicity for dosing of extended-release theophylline tablets. Sensitivity analyses revealed that patient compliance and content uniformity significantly influenced the probability of observing an adverse event. The Monte Carlo risk assessment platform defined the link between the critical quality attributes (CQAs) and clinical performance (i.e., performance-based quality specifications (PBQS)). The PBQS were subsequently utilized to generate process independent design spaces conditioned on inefficacy and toxicity risk. These design spaces, which directly account for the conditional relationships between product quality and patient variability, can be transferred to a specific process via models that relate process critical control parameters to the CQAs. Process Analytical Technology, therefore, can be integrated into the QbD production environment to control the safety and efficacy of the final product. This work demonstrated that process and product knowledge can be used to estimate the risk that final product quality imparts to clinical performance

Computer aided classification of histopathological damage in images of haematoxylin and eosin stained human skin

EngD ThesisExcised human skin can be used as a model to assess the potency, immunogenicity and contact sensitivity of potential therapeutics or cosmetics via the assessment of histological damage. The current method of assessing the damage uses traditional manual histological assessment, which is inherently subjective, time consuming and prone to intra-observer variability. Computer aided analysis has the potential to address issues surrounding traditional histological techniques through the application of quantitative analysis. This thesis describes the development of a computer aided process to assess the immune-mediated structural breakdown of human skin tissue. Research presented includes assessment and optimisation of image acquisition methodologies, development of an image processing and segmentation algorithm, identification and extraction of a novel set of descriptive image features and the evaluation of a selected subset of these features in a classification model. A new segmentation method is presented to identify epidermis tissue from skin with varying degrees of histopathological damage. Combining enhanced colour information with general image intensity information, the fully automated methodology segments the epidermis with a mean specificity of 97.7%, a mean sensitivity of 89.4% and a mean accuracy of 96.5% and segments effectively for different severities of tissue damage. A set of 140 feature measurements containing information about the tissue changes associated with different grades of histopathological skin damage were identified and a wrapper algorithm employed to select a subset of the extracted features, evaluating feature subsets based their prediction error for an independent test set in a Naïve Bayes Classifier. The final classification algorithm classified a 169 image set with an accuracy of 94.1%, of these images 20 were an unseen validation set for which the accuracy was 85.0%. The final classification method has a comparable accuracy to the existing manual method, improved repeatability and reproducibility and does not require an experienced histopathologist

Encapsulation of active compounds : particle characterization, loading efficiency and stability

Tese de doutoramento. Engenharia Química e Biológica. Universidade do Porto. Faculdade de Engenharia. 201

A Textbook of Advanced Oral and Maxillofacial Surgery

The scope of OMF surgery has expanded; encompassing treatment of diseases, disorders, defects and injuries of the head, face, jaws and oral cavity. This internationally-recognized specialty is evolving with advancements in technology and instrumentation. Specialists of this discipline treat patients with impacted teeth, facial pain, misaligned jaws, facial trauma, oral cancer, cysts and tumors; they also perform facial cosmetic surgery and place dental implants. The contents of this volume essentially complements the volume 1; with chapters that cover both basic and advanced concepts on complex topics in oral and maxillofacial surgery

Development and use of bioanalytical instrumentation and signal analysis methods for rapid sampling microdialysis monitoring of neuro-intensive care patients

This thesis focuses on the development and use of analysis tools to monitor brain injury patients. For this purpose, an online amperometric analyzer of cerebral microdialysis samples for glucose and lactate has been developed and optimized within the Boutelle group. The initial aim of this thesis was to significantly improve the signal-to-noise ratio and limit of detection of the assay to allow reliable quantification of the analytical data. The first approach was to re-design the electronic instrumentation of the assay. Printed-circuit boards were fabricated and proved very low noise, stable and much smaller than the previous potentiostats. The second approach was to develop generic data processing algorithms to remove three complex types of noise that commonly contaminate analytical signals: spikes, non-stationary ripples and baseline drift. The general strategy consisted in identifying the types of noise, characterising them, and subsequently subtracting them from the otherwise unprocessed data set. Spikes were effectively removed with 96.8% success and ripples were removed with minimal distortion of the signal resulting in an increased signal-to-noise ratio by up to 250%. This allowed reliable quantification of traces from ten patients monitored with the online microdialysis assay. Ninety-six spontaneous metabolic events in response to spreading depolarizations were resolved. These were characterized by a fall in glucose by -32.0 μM and a rise in lactate by +23.1 μM (median values) for over a 20-minute time-period. With frequently repeating events, this led to a progressive depletion of brain glucose. Finally, to improve the temporal coupling between the metabolic data and the electro-cortical signals, a flow-cell was engineered to integrate a potassium selective electrode into the microdialysate flow stream. With good stability over hours of continuous use and a 90% response time of 65 seconds, this flow cell was used for preliminary in vivo experiments the Max Planck Institute in Cologne