Usefulness of Bayesian networks in epidemiological studies

Introduction: Bayesian networks are a form of statistical modelling, which has been widely used in fields like clinical decision, systems biology, human immunodeficiency virus (HIV) and influenza research, analyses of complex disease systems, interactions between multiple diseases and, also, in diagnostic diseases. The present study aimed to show the usefulness of Bayesian networks (BNs) in epidemiological studies. Material and Methods: 3,993 subjects (men 1,758, women 2,235) belonging to the public productive sector from the Balearic Islands (Spain), which were active workers, constitute the data set. Results: A BN was built from a dataset composed of twelve relevant features in cardiovascular disease epidemiology. Furthermore, the structure and parameters were learnt with GeNIe 2.0 tool. Taking into account the main topological properties some features were optimized, obtaining a hypothesized scenario where the likelihoods of the different features were updated and the adequate conclusions were established. Conclusions: Bayesian networks allow us to obtain a hypothetical scenario where the probabilities of the different features are updated according to the evidence that is introduced. This fact makes Bayesian networks a very attractive tool.Introducción: Las redes Bayesianas son una forma de modelización estadística, las cuales han sido ampliamente utilizadas en campos como la decisión clínica, biología de sistemas, virus de inmunodeficiencia humana (VIH) e investigación en influenza, análisis de sistemas de enfermedades complejos, interacciones entre múltiples enfermedades y, también, en enfermedades de diagnóstico. Este estudio tiene como objetivo mostrar la utilidad de las redes Bayesianas en estudios epidemiológicos. Material y Métodos: 3,993 individuos (hombres 1,758, mujeres 2,235) pertenecientes al sector productivo público de las Islas Baleares (España), los cuales eran trabajadores activos, constituyen la base de datos. Resultados: Una red Bayesiana se ha obtenido a partir de una base de datos compuesta de doce características relevantes de la epidemiología de la enfermedad cardiovascular. Por otra parte, la estructura y los parámetros se han obtenido con la herramienta Genie 2.0. Teniendo en cuenta las principales propiedades topológicas algunas características fueron optimizadas. Conclusiones: Las redes Bayesianas permiten obtener un escenario hipotético donde las probabilidades de las diferentes características se van actualizando de acuerdo con la evidencia introducida. Este hecho hace de las redes Bayesianas una herramienta muy atractiva, además permite establecer diversas conclusiones

Application of a Nano-antimicrobial film to prevent ventilator-associated pneumonia: A pilot study

Ventilator-associated pneumonia (VAP) is one of the most common hospital-associated infections and has accounted for approximately 15% of all hospital-associated infections. In 76% of the VAP cases, the same bacteria colonize the oral cavity and lungs. Oral care interventions may play a role in the prevention of VAP, yet more than half of the hospitals do not have specific policies for the oral care of intubated patients. Oral cavity interlinks with respiratory tracts and digestive tracts. After surgery has been performed in these areas, aerobic and anaerobic bacteria frequently induce operative wound infections in teeth, gingiva and supporting tissues of the teeth and tonsils. This study investigates the effects of a nanotechnology antimicrobial spray (JUC) on the incidence of VAP. 320 patients diagnosed with VAP were randomly divided into treatment and control groups. After using chlorhexidine mouthrinse, the treatment group used a nanotechnology antimicrobial spray to the nose and mouth. The control group was given normal saline. The incidence rate of VAP was significantly lower in the treatment (8.38%) than control group (54.24%) (p<0.01). A physical antimicrobial film is formed on the surface of oral and nasal mucosa after using the JUC spray which effectively reduces the microbial colonization in the sprayed areas, thus reducing and delaying the incidence of VAP. © 2011 Academic Journals.published_or_final_versio

Cardiac health risk stratification system (CHRiSS): A Bayesian-based decision support system for left ventricular assist device (LVAD) therapy

This study investigated the use of Bayesian Networks (BNs) for left ventricular assist device (LVAD) therapy; a treatment for end-stage heart failure that has been steadily growing in popularity over the past decade. Despite this growth, the number of LVAD implants performed annually remains a small fraction of the estimated population of patients who might benefit from this treatment. We believe that this demonstrates a need for an accurate stratification tool that can help identify LVAD candidates at the most appropriate point in the course of their disease. We derived BNs to predict mortality at five endpoints utilizing the Interagency Registry for Mechanically Assisted Circulatory Support (INTERMACS) database: containing over 12,000 total enrolled patients from 153 hospital sites, collected since 2006 to the present day, and consisting of approximately 230 pre-implant clinical variables. Synthetic minority oversampling technique (SMOTE) was employed to address the uneven proportion of patients with negative outcomes and to improve the performance of the models. The resulting accuracy and area under the ROC curve (%) for predicted mortality were 30 day: 94.9 and 92.5; 90 day: 84.2 and 73.9; 6 month: 78.2 and 70.6; 1 year: 73.1 and 70.6; and 2 years: 71.4 and 70.8. To foster the translation of these models to clinical practice, they have been incorporated into a web-based application, the Cardiac Health Risk Stratification System (CHRiSS). As clinical experience with LVAD therapy continues to grow, and additional data is collected, we aim to continually update these BN models to improve their accuracy and maintain their relevance. Ongoing work also aims to extend the BN models to predict the risk of adverse events post-LVAD implant as additional factors for consideration in decision making

Building trajectories through clinical data to model disease progression

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Clinical trials are typically conducted over a population within a defined time period in order to illuminate certain characteristics of a health issue or disease process. These cross-sectional studies provide a snapshot of these disease processes over a large number of people but do not allow us to model the temporal nature of disease, which is essential for modeling detailed prognostic predictions. Longitudinal studies, on the other hand, are used to explore how these processes develop over time in a number of people but can be expensive and time-consuming, and many studies only cover a relatively small window within the disease process. This thesis describes the application of intelligent data analysis techniques for extracting information from time series generated by different diseases. The aim of this thesis is to identify intermediate stages in a disease process and sub-categories of the disease exhibiting subtly different symptoms. It explores the use of a bootstrap technique that fits trajectories through the data generating “pseudo time-series”. It addresses issues including: how clinical variables interact as a disease progresses along the trajectories in the data; and how to automatically identify different disease states along these trajectories, as well as the transitions between them. The thesis documents how reliable time-series models can be created from large amounts of historical cross-sectional data and a novel relabling/latent variable approach has enabled the exploration of the temporal nature of disease progression. The proposed algorithms are tested extensively on simulated data and on three real clinical datasets. Finally, a study is carried out to explore whether we can “calibrate” pseudo time-series models with real longitudinal data in order to improve them. Plausible directions for future research are discussed at the end of the thesis

Doctor of Philosophy

dissertationTemporal reasoning denotes the modeling of causal relationships between different variables across different instances of time, and the prediction of future events or the explanation of past events. Temporal reasoning helps in modeling and understanding interactions between human pathophysiological processes, and in predicting future outcomes such as response to treatment or complications. Dynamic Bayesian Networks (DBN) support modeling changes in patients' condition over time due to both diseases and treatments, using probabilistic relationships between different clinical variables, both within and across different points in time. We describe temporal reasoning and representation in general and DBN in particular, with special attention to DBN parameter learning and inference. We also describe temporal data preparation (aggregation, consolidation, and abstraction) techniques that are applicable to medical data that were used in our research. We describe and evaluate various data discretization methods that are applicable to medical data. Projeny, an opensource probabilistic temporal reasoning toolkit developed as part of this research, is also described. We apply these methods, techniques, and algorithms to two disease processes modeled as Dynamic Bayesian Networks. The first test case is hyperglycemia due to severe illness in patients treated in the Intensive Care Unit (ICU). We model the patients' serum glucose and insulin drip rates using Dynamic Bayesian Networks, and recommend insulin drip rates to maintain the patients' serum glucose within a normal range. The model's safety and efficacy are proven by comparing it to the current gold standard. The second test case is the early prediction of sepsis in the emergency department. Sepsis is an acute life threatening condition that requires timely diagnosis and treatment. We present various DBN models and data preparation techniques that detect sepsis with very high accuracy within two hours after the patients' admission to the emergency department. We also discuss factors affecting the computational tractability of the models and appropriate optimization techniques. In this dissertation, we present a guide to temporal reasoning, evaluation of various data preparation, discretization, learning and inference methods, proofs using two test cases using real clinical data, an open-source toolkit, and recommend methods and techniques for temporal reasoning in medicine

A dynamic Bayesian network for diagnosing ventilator-associated pneumonia in ICU patients

Contains fulltext : 76095.pdf (publisher's version ) (Closed access)10 p

Medicina Balear 2014, vol. 29, n. 3

Medicina Balear, òrgan de la Reial Acadèmia de Medicina de les Illes Balears, publica en català, castellà o anglès treballs originals, articles de revisió, cartes al director i altres escrits d'interès relacionats amb les ciències de la salut i presta particular atenció als treballs que tinguin per àmbit les Illes Balears i altres territoris de la conca mediterrània occidental. La revista sotmet els manuscrits a la revisió anònima per al menys dos experts externs (peer review