A nonparametric regression cross spectrum for multivariate time series

We consider dependence structures in multivariate time series that are characterized by deterministic trends. Results from spectral analysis for stationary processes are extended to deterministic trend functions. A regression cross covariance and spectrum are defined. Estimation of these quantities is based on wavelet thresholding. The method is illustrated by a simulated example and a three-dimensional time series consisting of ECG, blood pressure and cardiac stroke volume measurements.Nonparametric trend estimation, cross spectrum, wavelets, regression spectrum, phase, threshold estimator

Estimation of a nonparametric regression spectrum for multivariate time series

Estimation of a nonparametric regression spectrum based on the periodogram is considered. Neither trend estimation nor smoothing of the periodogram are required. Alternatively, for cases where spectral estimation of phase shifts fails and the shift does not depend on frequency, a time domain estimator of the lag-shift is defined. Asymptotic properties of the frequency and time domain estimators are derived. Simulations and a data example illustrate the methods.Periodogram, cross spectrum, regression spectrum, phase, wavelets.

Clinical decision support using Open Data

First Online: 18 May 2020.The growth of Electronical Health Records (EHR) in healthcare has been gradual. However, a simple EHR system has become inefficient in supporting health professionals on decision making. In this sense, the need to acquire knowledge from storing data using open models and techniques has emerged, for the sake of improving the quality of service provided and to support the decision-making process. The usage of open models promotes interoperability between systems, communicating more efficiently. In this sense, the OpenEHR open data approach is applied, modelling data in two levels to distinguish knowledge from information. The application of clinical terminologies was fundamental in this study, in order to control data semantics based on coded clinical terms. This article culminated from the conceptualization of the knowledge acquisition process to represent Clinical Decision Support, using open data models.The work has been supported by FCT–Fundação para a Ciência e Tec-nologia within the Project Scope UID/CEC/00319/2019 and DSAIPA/DS/0084/2018

Toward the use of upper level ontologies for semantically interoperable systems: an emergency management use case

In the context of globalization and knowledge management, information technologies require an ample need of unprecedented levels of data exchange and sharing to allow collaboration between heterogeneous systems. Yet, understanding the semantics of the exchanged data is one of the major challenges. Semantic interoperability can be ensured by capturing knowledge from diverse sources by using ontologies and align these latter by using upper level ontologies to come up with a common shared vocabulary. In this paper, we aim in one hand to investigate the role of upper level ontologies as a mean for enabling the formalization and integration of heterogeneous sources of information and how it may support interoperability of systems. On the other hand, we present several upper level ontologies and how we chose and then used Basic Formal Ontology (BFO) as an upper level ontology and Common Core Ontology (CCO) as a mid-level ontology to develop a modular ontology that define emergency responders’ knowledge starting from firefighters’ module for a solution to the semantic interoperability problem in emergency management

Systematic population-wide ecological analysis of regional variability in disease prevalence

The prevalence of diseases often varies substantially from region to region. Besides basic demographic properties, the factors that drive the variability of each prevalence are to a large extent unknown. Here we show how regional prevalence variations in 115 different diseases relate to demographic, socio-economic, environmental factors and migratory background, as well as access to different types of health services such as primary, specialized and hospital healthcare. We have collected regional data for these risk factors at different levels of resolution; from large regions of care (Versorgungsregion) down to a 250 by 250 m square grid. Using multivariate regression analysis, we quantify the explanatory power of each independent variable in relation to the regional variation of the disease prevalence. We find that for certain diseases, such as acute heart conditions, diseases of the inner ear, mental and behavioral disorders due to substance abuse, up to 80% of the variance can be explained with these risk factors. For other diagnostic blocks, such as blood related diseases, injuries and poisoning however, the explanatory power is close to zero. We find that the time needed to travel from the inhabited center to the relevant hospital ward often contributes significantly to the disease risk, in particular for diabetes mellitus. Our results show that variations in disease burden across different regions can for many diseases be related to variations in demographic and socio-economic factors. Furthermore, our results highlight the relative importance of access to health care facilities in the treatment of chronic diseases like diabetes