When modeling longitudinal biomedical data, often dimensionality reduction as well as dynamic modeling in the resulting latent representation is needed. This can be achieved by artificial neural networks for dimension...
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Viral outbreaks, such as the COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown a...
Viral outbreaks, such as the COVID-19 pandemic, are commonly described by compartmental models by means of ordinary differential equation (ODE) systems. The parameter values of these ODE models are typically unknown and need to be estimated based on accessible data. In order to describe realistic pandemic scenarios with strongly varying situations, these model parameters need to be assumed as time-dependent. While parameter estimation for the typical case of time-constant parameters does not pose larger issues, the determination of time-dependent parameters, e.g. the transition rates of compartmental models, remains notoriously difficult, since the function class of these time-dependent parameters is ***, we propose a combination of an Augmented Kalman Smoother with an Expectation-Maximization algorithm to simultaneously estimate all time-dependent parameters in an SIRD compartmental model. The approach is applicable to general systems of ODEs with time-varying parameters. In particular, it does not require prior knowledge on model parameters or any further assumptions on the function class of the time-dependencies of the ODE. In contrast to other approaches, no assumptions on the parameterization of the serial interval distribution are required for the estimation of SIRD-model parameters. With the presented method, we are able to adequately describe time series of COVID-19 data in Germany including strong fluctuations and multiple waves, and to give non-parametric model-based time course estimates for the effective reproduction number.
The population-attributable fraction (PAF) is a popular epidemiological measure for the burden of a harmful exposure within a population. It is often interpreted causally as proportion of preventable cases after an el...
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Background: Up to 30 % of pregnant individuals experience high levels of stress. At the same time, 15–20 % of new mothers develop postpartum depression, and 25–35 % experience postpartum anxiety. Mobile applications...
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The public health impact of a harmful exposure can be quantified by the population-attributable fraction (PAF). The PAF describes the attributable risk due to an exposure and is often interpreted as the proportion of ...
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The discovery of clinical biomarkers requires large patient cohorts and is aided by a pooled data approach across institutions. In many countries, data protection constraints, especially in the clinical environment, f...
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The population-attributable fraction (PAF) quantifies the public health impact of a harmful exposure. Despite being a measure of significant importance an estimand accommodating complicated time-to-event data is not c...
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Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy;seizure prediction ba...
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Abstract Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy; seizure pre...
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Abstract Digital signal processing of Electroencephalogram (EEG) can support the diagnosis and alarming for the benefit of humans. About one third of all epileptic patients suffer from refractory epilepsy; seizure prediction based on the EEG information content is an area of intense activity since at least twenty years. In this paper we analyze the high dimensional feature space created by a variety of feature extraction methods for prediction of epileptic seizures. We combined features selection algorithm minimum redundancy maximum relevance (mRMR) and Support Vector Machines (SVMs) architectures to study the best features set for seizure prediction. We present the comparison between the classification results obtained by a feature set composed by 147 features and a reduced set based on the first 20-ranked features using mRMR scores. We critically discuss the composition of the feature subset. The results suggest some patient specificity in features and channel selection. The best models lead us to hypothesize the preference for wider preictal periods.
Mathematical modelling of infectious diseases gains growing attention in epidemiology during the last decades. The major benefits of simulating compartmental models are the prediction of the consequences of potential ...
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