Parental attitudes and opinions on the use of psychotropic medication in mental disorders of childhood

<p>Abstract</p> <p>Background</p> <p>The limited number of systematic, controlled studies that assess the safety and efficacy of psychotropic medications for children reinforce the hesitation and reluctance of parents to administer such medications. The aim of this study was to investigate the attitudes of parents of children with psychiatric disorders, towards psychotropic medication.</p> <p>Methods</p> <p>A 20-item questionnaire was distributed to 140 parents during their first contact with an outpatient child psychiatric service. The questionnaire comprised of questions regarding the opinions, knowledge and attitudes of parents towards children's psychotropic medication. Sociodemographic data concerning parents and children were also recorded. Frequency tables were created and the chi-square test and Fisher's exact tests were used for the comparison of the participants' responses according to sex, educational level, age and gender of the child and use of medication.</p> <p>Results</p> <p>Respondents were mostly mothers aged 25–45 years. Children for whom they asked for help with were mostly boys, aged between 6 and 12 years old. A total of 83% of the subjects stated that they knew psychotropic drugs are classified into categories, each having a distinct mechanism of action and effectiveness. A total of 40% believe that there is a proper use of psychotropic medication, while 20% believe that psychiatrists unnecessarily use high doses of psychotropic medication. A total of 80% fear psychotropic agents more than other types of medication. Most parents are afraid to administer psychotropic medication to their child when compared to any other medication, and believe that psychotherapy is the most effective method of dealing with every kind of mental disorders, including childhood schizophrenia (65%). The belief that children who take psychotropic medication from early childhood are more likely to develop drug addiction later is correlated with the parental level of education.</p> <p>Conclusion</p> <p>Parents' opinions and beliefs are not in line with scientific facts. This suggests a need to further inform the parents on the safety and efficacy of psychotropic medication in order to improve treatment compliance.</p

Parallel model exploration for tumor treatment simulations

Abstract Computational systems and methods are often being used in biological research, including the understanding of cancer and the development of treatments. Simulations of tumor growth and its response to different drugs are of particular importance, but also challenging complexity. The main challenges are first to calibrate the simulators so as to reproduce real-world cases, and second, to search for specific values of the parameter space concerning effective drug treatments. In this work, we combine a multi-scale simulator for tumor cell growth and a genetic algorithm (GA) as a heuristic search method for finding good parameter configurations in reasonable time. The two modules are integrated into a single workflow that can be executed in parallel on high performance computing infrastructures. In effect, the GA is used to calibrate the simulator, and then to explore different drug delivery schemes. Among these schemes, we aim to find those that minimize tumor cell size and the probability of emergence of drug resistant cells in the future. Experimental results illustrate the effectiveness and computational efficiency of the approach.This work has received funding from the EU Horizon 2020 RIA program INFORE under grant agreement No 825070Peer ReviewedPostprint (author's final draft

Complex event forecasting with prediction suffix trees

Complex event recognition (CER) systems have become popular in the past two decades due to their ability to “instantly” detect patterns on real-time streams of events. However, there is a lack of methods for forecasting when a pattern might occur before such an occurrence is actually detected by a CER engine. We present a formal framework that attempts to address the issue of complex event forecasting (CEF). Our framework combines two formalisms: (a) symbolic automata which are used to encode complex event patterns and (b) prediction suffix trees which can provide a succinct probabilistic description of an automaton’s behavior. We compare our proposed approach against state-of-the-art methods and show its advantage in terms of accuracy and efficiency. In particular, prediction suffix trees, being variable-order Markov models, have the ability to capture long-term dependencies in a stream by remembering only those past sequences that are informative enough. We also discuss how CEF solutions should be best evaluated on the quality of their forecasts

A distributed online learning approach for pattern prediction over movement event streams with apache flink

In this paper, we present a distributed online prediction system for user-defined patterns over multiple massive streams of movement events, built using the general purpose stream processing framework Apache Flink. The proposed approach is based on combining probabilistic event pattern prediction models on multiple predictor nodes with a distributed online learning protocol in order to continuously learn the parameters of a global prediction model and share them among the predictors in a communication-efficient way. Our approach enables the collaborative learning between the predictors (i.e., "learn from each other"), thus the learning rate is accelerated with less data for each predictor. The underlying model provides online predictions about when a pattern (i.e., a regular expression ove r the event types) is expected to be completed within each event stream. We describe the distributed architecture of the proposed system, its implementation in Flink, and present experimental results over real-world event streams related to trajectories of moving vessels

Experimental Comparison of Complex Event Processing Systems in the Maritime Domain

Complex Event Processing (CEP) `s main purpose is recognizing interesting phenomena upon streams of data. So its only natural that it would find applications in the maritime domain, where detecting vessel activity plays an important role in monitoring movement at sea. In this study we briefly examine the field of Complex Event Processing; we present two CEP implementations, one based on machine learning techniques and a rule-based system modeled with Event Calculus. Finally, we evaluate their ability in modeling activities that involve multiple vessels, by comparing their results on real-life examples

Proactive Streaming Analytics at Scale: A Journey from the State-of-the-art to a Production Platform

&lt;p&gt;Proactive streaming analytics continuously extract real-time business value from massive data that stream in data centers or clouds. This requires (a) to process the data while they are still in motion; (b) to scale the processing to multiple machines, often over various, dispersed computer clusters, with diverse Big Data technologies; and (c) to forecast complex business events for proactive decision-making. Combining the necessary facilities for proactive streaming analytics at scale entails: (I) deep knowledge of the relevant state-of-the-art, (II) cherry-picking cutting edge research outcomes based on desired features and with the prospect of building interoperable components, and (III) building components and deploying them into a holistic architecture within a real-world platform. In this tutorial, we drive the audience through the whole journey from (I) to (III), delivering cutting edge research into a commercial analytics platform, for which we provide a hands-on experience.&lt;/p&gt;&lt;p&gt;Video Teaser Link available at ACM DL: https://dl.acm.org/doi/10.1145/3583780.3615293&lt;br&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&lt;i&gt;The permanent web page of this tutorial: https://www.softnet.tuc.gr/en/research/journeycikm23tutorial provides additional material including the tutorial slides, open source code repositories of the developed platform, and use case related videos.&lt;/i&gt;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt;&lt;p&gt;&nbsp;&lt;/p&gt

Composite Event Patterns for Maritime Monitoring

Maritime monitoring systems support safe shipping as they allow for the real-time detection of dangerous, suspicious and illegal vessel activities. We have been developing a composite event recognition system for maritime monitoring in the Event Calculus, allowing both for verification and real-time performance. To increase the accuracy of the system, we have been collaborating with domain experts in order to construct effective patterns of maritime activity. We present some indicative patterns in the Event Calculus, and evaluate them using two forms of real kinematic vessel data