A Neural Networks Committee for the Contextual Bandit Problem

This paper presents a new contextual bandit algorithm, NeuralBandit, which does not need hypothesis on stationarity of contexts and rewards. Several neural networks are trained to modelize the value of rewards knowing the context. Two variants, based on multi-experts approach, are proposed to choose online the parameters of multi-layer perceptrons. The proposed algorithms are successfully tested on a large dataset with and without stationarity of rewards.Comment: 21st International Conference on Neural Information Processin

Clinical Pharmacology Studies in Critically Ill Children

Developmental and physiological changes in children contribute to variation in drug disposition with age. Additionally, critically ill children suffer from various life-threatening conditions that can lead to pathophysiological alterations that further affect pharmacokinetics (PK). Some factors that can alter PK in this patient population include variability in tissue distribution caused by protein binding changes and fluid shifts, altered drug elimination due to organ dysfunction, and use of medical interventions that can affect drug disposition (e.g., extracorporeal membrane oxygenation and continuous renal replacement therapy). Performing clinical studies in critically ill children is challenging because there is large inter-subject variability in the severity and time course of organ dysfunction; some critical illnesses are rare, which can affect subject enrollment; and critically ill children usually have multiple organ failure, necessitating careful selection of a study design. As a result, drug dosing in critically ill children is often based on extrapolations from adults or non-critically ill children. Dedicated clinical studies in critically ill children are urgently needed to identify optimal dosing of drugs in this population. This review will summarize the effect of critical illness on pediatric PK, the challenges associated with performing studies in this vulnerable subpopulation, and the clinical PK studies performed to date for commonly used drugs

Infering Air Quality from Traffic Data using Transferable Neural Network Models

This work presents a neural network based model for inferring air quality from traffic measurements. It is important to obtain information on air quality in urban environments in order to meet legislative and policy requirements. Measurement equipment tends to be expensive to purchase and maintain. Therefore, a model based approach capable of accurate determination of pollution levels is highly beneficial. The objective of this study was to develop a neural network model to accurately infer pollution levels from existing data sources in Leicester, UK. Neural Networks are models made of several highly interconnected processing elements. These elements process information by their dynamic state response to inputs. Problems which were not solvable by traditional algorithmic approaches frequently can be solved using neural networks. This paper shows that using a simple neural network with traffic and meteorological data as inputs, the air quality can be estimated with a good level of generalisation and in near real-time. By applying these models to links rather than nodes, this methodology can directly be used to inform traffic engineers and direct traffic management decisions towards enhancing local air quality and traffic management simultaneously.Universidad de Málaga. Campus de Excelencia Internacional Andalucía Tech

Applying machine learning to the problem of choosing a heuristic to select the variable ordering for cylindrical algebraic decomposition

Cylindrical algebraic decomposition(CAD) is a key tool in computational algebraic geometry, particularly for quantifier elimination over real-closed fields. When using CAD, there is often a choice for the ordering placed on the variables. This can be important, with some problems infeasible with one variable ordering but easy with another. Machine learning is the process of fitting a computer model to a complex function based on properties learned from measured data. In this paper we use machine learning (specifically a support vector machine) to select between heuristics for choosing a variable ordering, outperforming each of the separate heuristics.Comment: 16 page

BAMBI: blind accelerated multimodal Bayesian inference

In this paper we present an algorithm for rapid Bayesian analysis that combines the benefits of nested sampling and artificial neural networks. The blind accelerated multimodal Bayesian inference (BAMBI) algorithm implements the MultiNest package for nested sampling as well as the training of an artificial neural network (NN) to learn the likelihood function. In the case of computationally expensive likelihoods, this allows the substitution of a much more rapid approximation in order to increase significantly the speed of the analysis. We begin by demonstrating, with a few toy examples, the ability of a NN to learn complicated likelihood surfaces. BAMBI's ability to decrease running time for Bayesian inference is then demonstrated in the context of estimating cosmological parameters from Wilkinson Microwave Anisotropy Probe and other observations. We show that valuable speed increases are achieved in addition to obtaining NNs trained on the likelihood functions for the different model and data combinations. These NNs can then be used for an even faster follow-up analysis using the same likelihood and different priors. This is a fully general algorithm that can be applied, without any pre-processing, to other problems with computationally expensive likelihood functions.Comment: 12 pages, 8 tables, 17 figures; accepted by MNRAS; v2 to reflect minor changes in published versio

Risk Factors and In-Hospital Outcomes following Tracheostomy in Infants

To describe the epidemiology, risk factors, and in-hospital outcomes of tracheostomy in infants in the neonatal intensive care unit (NICU)

Tracheostomy after Surgery for Congenital Heart Disease: An Analysis of the Society of Thoracic Surgeons Congenital Heart Surgery Database

Background Information concerning tracheostomy after operations for congenital heart disease has come primarily from single-center reports. We aimed to describe the epidemiology and outcomes associated with postoperative tracheostomy in a multi-institutional registry. Methods The Society of Thoracic Surgeons Congenital Heart Database (2000 to 2014) was queried for all index operations with the adverse event “postoperative tracheostomy” or “respiratory failure, requiring tracheostomy.” Patients with preoperative tracheostomy or weighing less than 2.5 kg undergoing isolated closure of patent ductus arteriosus were excluded. Trends in tracheostomy incidence over time from January 2000 to June 2014 were analyzed with a Cochran-Armitage test. The patient characteristics associated with operative mortality were analyzed for January 2010 to June 2014, including deaths occurring up to 6 months after transfer of patients to long-term care facilities. Results From 2000 to 2014, the incidence of tracheostomy after operations for congenital heart disease increased from 0.11% in 2000 to a high of 0.76% in 2012 (p < 0.0001). From 2010 to 2014, 648 patients underwent tracheostomy. The median age at operation was 2.5 months (25th, 75th percentile: 0.4, 7). Prematurity (n = 165, 26%), genetic abnormalities (n = 298, 46%), and preoperative mechanical ventilation (n = 275, 43%) were common. Postoperative adverse events were also common, including cardiac arrest (n = 131, 20%), extracorporeal support (n = 87, 13%), phrenic or laryngeal nerve injury (n = 114, 18%), and neurologic deficit (n = 51, 8%). The operative mortality was 25% (n = 153). Conclusions Tracheostomy as an adverse event of operations for congenital heart disease remains rare but has been increasingly used over the past 15 years. This trend and the considerable mortality risk among patients requiring postoperative tracheostomy support the need for further research in this complex population

How best to Design Fuzzy Sets and Systems:In memory of Prof. Lotfi A. Zadeh

The fundamental shift in dealing with uncertainties [12] and computerised reasoning was made by the late Professor Lotfi Aliasker Zadeh (1921–2017) in 1965 in his seminal paper [1]. For the last over five decades the Fuzzy Sets theory has matured and was applied to a long list of applications spanning from engineering, social sciences, biology to transport, mathematics and many mor

Consumer behaviour in the waiting area

Objective of the study: To determine consumer behaviour in the pharmacy waiting area. Method: The applied methods for data-collection were direct observations. Three Dutch community pharmacies were selected for the study. The topics in the observation list were based on available services at each waiting area (brochures, books, illuminated new trailer, children’s play area, etc.). Per patient each activity was registered, and at each pharmacy the behaviour was studied for 2 weeks. Results: Most patients only waited during the waiting time at the studied pharmacies. Few consumers obtained written information during their wait. Conclusion: The waiting area may have latent possibilities to expand the information function of the pharmacy and combine this with other activities that distract the consumer from the wait. Transdisciplinary research, combining knowledge from pharmacy practice research with consumer research, has been a useful approach to add information on queueing behaviour of consumers