Search results for "machine"
showing 10 items of 2592 documents
Use of the KSVM-based system for the definition, validation and identification of the incisional hernia recurrence risk factors
2019
BACKGROUND: Incisional hernia is one of the most common complications after abdominal surgery with an incidence rate of 11 to 20% post laparotomy. Many different factors can be considered as risk factors of incisional hernia recurrence. The aim of this study is to confirm and to validate the incisional hernia recurrence risk factors and to identify and to validate new ones. METHODS: In the period from July 2007 to July 2017, 154 patients were selected and subjected to incisional hernia repair. The surgical operations were conducted under general anaesthesia. Patients received antibiotic prophylaxis when indicated, according to the hospital prophylaxis scheme. Inclusion criteria of the study…
Modeling crowd dynamics through coarse-grained data analysis
2018
International audience; Understanding and predicting the collective behaviour of crowds is essential to improve the efficiency of pedestrian flows in urban areas and minimize the risks of accidents at mass events. We advocate for the development of crowd traffic management systems, whereby observations of crowds can be coupled to fast and reliable models to produce rapid predictions of the crowd movement and eventually help crowd managers choose between tailored optimization strategies. Here, we propose a Bi-directional Macroscopic (BM) model as the core of such a system. Its key input is the fundamental diagram for bi-directional flows, i.e. the relation between the pedestrian fluxes and d…
IMI – Oral biopharmaceutics tools project – Evaluation of bottom-up PBPK prediction success part 4: Prediction accuracy and software comparisons with…
2020
Oral drug absorption is a complex process depending on many factors, including the physicochemical properties of the drug, formulation characteristics and their interplay with gastrointestinal physiology and biology. Physiological-based pharmacokinetic (PBPK) models integrate all available information on gastro-intestinal system with drug and formulation data to predict oral drug absorption. The latter together with in vitro-in vivo extrapolation and other preclinical data on drug disposition can be used to predict plasma concentration-time profiles in silico. Despite recent successes of PBPK in many areas of drug development, an improvement in their utility for evaluating oral absorption i…
Network reconstruction for trans acting genetic loci using multi-omics data and prior information.
2022
Background: Molecular measurements of the genome, the transcriptome, and the epigenome, often termed multi-omics data, provide an in-depth view on biological systems and their integration is crucial for gaining insights in complex regulatory processes. These data can be used to explain disease related genetic variants by linking them to intermediate molecular traits (quantitative trait loci, QTL). Molecular networks regulating cellular processes leave footprints in QTL results as so-called trans-QTL hotspots. Reconstructing these networks is a complex endeavor and use of biological prior information can improve network inference. However, previous efforts were limited in the types of priors…
Context-related data processing in artificial neural networks for higher reliability of telerehabilitation systems
2015
Classification is a data processing technique of a great significance both for native eHealth systems and web telemedicine solutions. In this sense, artificial neural networks have been widely applied in telerehabilitation as powerful tools to process information and acquire a new medical knowledge. But effective analysis of multidimensional heterogeneous medical data, still poses considerable difficulties. It was shown that processing too many data features simultaneously is costly and has some adverse effects on the resulting models classification properties. Therefore, there is a strong need to develop new techniques for selecting features from the very large data sets that include many …
Machine learning in remote sensing data processing
2009
Remote sensing data processing deals with real-life applications with great societal values. For instance urban monitoring, fire detection or flood prediction from remotely sensed multispectral or radar images have a great impact on economical and environmental issues. To treat efficiently the acquired data and provide accurate products, remote sensing has evolved into a multidisciplinary field, where machine learning and signal processing algorithms play an important role nowadays. This paper serves as a survey of methods and applications, and reviews the latest methodological advances in machine learning for remote sensing data analysis.
A Special Issue on Advances in Machine Learning for Remote Sensing and Geosciences [From the Guest Editors]
2016
Machine learning has become a standard paradigm for the analysis of remote sensing and geoscience data at both local and global scales. In the upcoming years, with the advent of new satellite constellations, machine learning will have a fundamental role in processing large and heterogeneous data sources. Machine learning will move from mere statistical data processing to actual learning, understanding, and knowledge extraction. The ambitious goal is to provide responses to the challenging scientific questions about the earth system. This special issue aims at providing an updated, refreshing view of current developments in the field. For this special issue, we have collected five articles t…
EHRtemporalVariability
2020
Functions to delineate temporal dataset shifts in Electronic Health Records through the projection and visualization of dissimilarities among data temporal batches. This is done through the estimation of data statistical distributions over time and their projection in non-parametric statistical manifolds, uncovering the patterns of the data latent temporal variability. EHRtemporalVariability is particularly suitable for multi-modal data and categorical variables with a high number of values, common features of biomedical data where traditional statistical process control or time-series methods may not be appropriate. EHRtemporalVariability allows you to explore and identify dataset shifts t…
Diagnóstico de Enfermedades Card´ıacas con los algoritmos supervisados Naives Bayesian
2020
Las enfermedades cardíacas son la principal causa de muerte en la actualidad. Este paper contrasta la performance de los diferentes algoritmos supervisados de Machine Learning, que tienen aplicaciones en el a´rea de la medicina, con los algoritmos supervisados Naives Bayes para ayudar a clasificar pacientes propensos a sufrir enfermedades cardíacas. Como fuente de datos se usan 303 instancias de pacientes con diferentes características que fueron analizados al procesar los datos con los respectivos algoritmos. Los resultados con el algoritmo de Naives Bayes son pro- metedores, obteniendo una precisio´n del 86,81 %, usando la fuente de datos mencionada. Esta familia de algoritmos tiene un me…
Integrating LSTMs with Online Density Estimation for the Probabilistic Forecast of Energy Consumption
2019
In machine learning applications in the energy sector, it is often necessary to have both highly accurate predictions and information about the probabilities of certain scenarios to occur. We address this challenge by integrating and combining long short-term memory networks (LSTMs) and online density estimation into a real-time data streaming architecture of an energy trader. The online density estimation is done in the MiDEO framework, which estimates joint densities of data streams based on ensembles of chains of Hoeffding trees. One attractive feature of the solution is that queries can be sent to the here-called forecast-based point density estimators (FPDE) to derive information from …