Weaning outcome prediction from heterogeneous time series using Normalized Compression Distance and Multidimensional Scaling

Abstract

In the Intensive Care Unit of a hospital (ICU), weaning can be defined as the process of gradual reduction in the level of mechanical ventilation support. A failed weaning increases the risk of death in prolonged mechanical ventilation patients. Different methods for weaning outcome prediction have been proposed using variables and time series extracted from the monitoring systems, however, monitored data are often non-regularly sampled, hence limiting its use in conventional automatic prediction systems. In this work, we propose the joint use of two statistical techniques, Normalized Compression Distance (NCD) and Multidimensional Scaling (MDS), to deal with data heterogeneity in monitoring systems for weaning outcome prediction. A total of 104 weanings were selected from 93 patients under mechanical ventilation from the ICU of Hospital Universitario Fundación Alcorcón; for each weaning, time series (TS), clinical laboratory and general descriptors variables were collected during 48 hours previous to the moment of withdrawal mechanical support (extubation). The TS diastolic blood pressure variable provided the best weaning prediction, with an

Impact of age, race and socio-economic status on temporal trends in late-stage prostate cancer diagnosis in Florida

“…To visualize the impact of race and socio-economic status on temporal trends, we propose to project the ensemble of time series into 2D space using metric multi-dimensional scaling (MDS). The application of MDS to time series is fairly recent (Bernard et al, 2012) and applications to health data are rare (Lillo-Castellano et al, 2013). Two new metrics are here introduced to quantify the similarity between the magnitude and rate of changes of time series of health outcomes.…”

Rotulação automática de clusters baseada em análise de filogramas

“…A NCDé baseada em outra métrica chamada NID (do inglês, Normalized Information Distance) [17], que considera a semelhança entre as variáveis de acordo com a característica dominante que elas compartilham. No entanto, a NID utiliza diretamente o conceito de complexidade de Kolmogorov [18] no cálculo da distância, queé computacionalmente inviável para amostras grandes.…”

New approach for multidimensional prognostic of stochastic time series based on sparse-based fractional Levy quaternion extended Kalman filter

“…Therefore, time-series modeling and forecasting methodology have attracted significant attention in the communities of knowledge engineering, data-based science, and artificial intelligence community, etc. 4–7…”

“…Therefore, time-series modeling and forecasting methodology have attracted significant attention in the communities of knowledge engineering, data-based science, and artificial intelligence community, etc. [4][5][6][7] During the past few decades, to model nonlinearity of stochastic time-series accurately, tremendous types of prognostic methods/models/algorithms/techniques are reported, for example, some prognostic methods that focus on single-channel data such as autoregressive integrated moving average (ARIMA), 8,9 long-range dependence, 10,11 fractional Brownian motion, 12,13 particle filter (PF), 14,15 stochastic differential equation, 16 but they ignore the mutual information (e.g., shaft centerline orbit) and spatial statistical properties (e.g., autocorrelation coefficient) between each channel and have obvious insufficiency in dealing with hyperdimension signals. Fortunately, instead of treating each dimensional data individually, issues aforementioned can be alleviated via some prognostic methods that focus on multichannel data, for example, neural networks (NNs), 17,18 multilayer perceptron networks, 19 deep neural networks, 20,21 have been reported currently.…”