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Diffuse low-grade gliomas are rare primitive cerebral tumours of adults. These tumors progress continuously over time and then turn to a higher grade of malignancy associated with neurological disability, leading ultimately to death. Tumour size is one of the most important prognostic factors. Thus, it is of great importance to be able to assess the volume of the tumor during the patients' monitoring...
The onset of fetal pathologies can be screened during pregnancy by means of Fetal Heart Rate (FHR) monitoring and analysis. Noticeable advances in understanding FHR variations were obtained in the last twenty years, thanks to the introduction of quantitative indices extracted from the FHR signal. This study searches for discriminating Normal and Intra Uterine Growth Restricted (IUGR) fetuses by applying...
Stroke is a major cause of mortality and long-term disability in the world. Predictive outcome models in stroke are valuable for personalized treatment, rehabilitation planning and in controlled clinical trials. We design a new multi-class classification model to predict outcome in the short-term, the putative therapeutic window for several treatments. Our model addresses the challenges of class imbalance,...
A sparse Laguerre-Volterra autoregressive model has been developed as feature extraction from subdural human EEG data for seizure prediction in temporal lobe epilepsy. The use of Laguerre-Volterra kernel can compactly yield an autoregressive model of longer system memory without increasing the number of the coefficients. In 6 sets of seizure, we used a sparse Laguerre-Volterra autoregressive model...
Information in Electronic Medical Records (EMRs) can be used to generate accurate predictions for the occurrence of a variety of health states, which can contribute to more pro-active interventions. The very nature of EMRs does make the application of off-the-shelf machine learning techniques difficult. In this paper, we study two approaches to making predictions that have hardly been compared in...
Pan-cancer analyses attempt to discover similar features among multiple cancers to identify fundamental patterns common to cancer development and progression. A pan-cancer analysis integrating both protein expression and transcriptomic data is important because it can identify genes that are linked to proteins potentially responsible for a patient's status. This study aims to identify differentially...
The technologies for continuous measurement of the anaesthetic agents circulating in body fluids are not mature yet, though some preliminary prototypes exist already. We present a control algorithm that based on the real measurement of propofol plasma concentration may adjust the delivery rate. This opens a possibility for a safer anesthesia when the technologies for online measurement of drug concentration...
Human variability in electroencephalogram (EEG) poses significant challenges for developing practical real-world applications of brain-computer interfaces (BCIs). The intuitive solution of collecting sufficient user-specific training/calibration data can be very labor-intensive and time-consuming, hindering the practicability of BCIs. To address this problem, transfer learning (TL), which leverages...
Valve-sparing aortic root reconstruction is an up- and-coming approach for patients suffering from aortic valve insufficiencies which promises to significantly reduce complications. However, the success of the treatment strongly depends on the challenging task of choosing the correct size of the prosthesis, for which, up to now, surgeons solely have to rely on their experience. Here, we present a...
In this work, we propose a feature exploration method for learning-based cuffless blood pressure measurement. More specifically, to efficiently explore a large feature space from the photoplethysmography signal, we have applied several analytical techniques, including random error elimination, adaptive outlier removal, maximum information coefficient and Pearson's correlation coefficient based feature...
Biomarker discovery involves finding correlations between features and clinical symptoms to aid clinical decision. This task is especially difficult in resting state functional magnetic resonance imaging (rs-fMRI) data due to low SNR, high-dimensionality of images, inter-subject and intra-subject variability and small numbers of subjects compared to the number of derived features. Traditional univariate...
HIV-1 vaccine injection has been shown less effective due to the diversity of antigens. Increasing the knowledge of the associations between immune system and virus would ultimately result in producing effective vaccines against HIV-1 virus. To increase the understanding of immunological information, computational models can be utilised to construct predictive models. The aim of this study is, therefore,...
A wide range of assistive technologies have been developed to support the elderly population with the goal of promoting independent living. The adoption of these technology based solutions is, however, critical to their overarching success. In our previous research we addressed the significance of modelling user adoption to reminding technologies based on a range of physical, environmental and social...
There is a discontinuity in published electromechanical delays (EMD) in upper-limb muscles and the state-of-the-art in modelling end-point force from electromyographic signals collected from one or more muscles. Published values are typically in the range of 10 to 30ms, depending on the nature of the contraction. In published literature where the EMG-force relationship is modelled, generally a delay...
Proteins interact with other proteins and bio-molecules to carry out biological processes in a cell. Computational models help understanding complex biochemical processes that happens throughout the life of a cell. Domainmediated protein interaction to peptides one such complex problem in bioinformatics that requires computational predictive models to identify meaningful bindings. In this study, domain-peptide...
Synthetic genetic circuits sometimes exhibit unexpected functionality or even fail entirely when implemented in vivo, due to the effects of interactions with the host cell that were not accounted for in the circuit's design. In this paper, we consider the effects that limitations in cellular resources have on the dynamics of a synthetic cellular oscillator. We show that incorporating these effects...
We present a Recurrent Neural Network using LSTM (Long Short Term Memory) that is capable of modeling and predicting Local Field Potentials. We train and test the network on real data recorded from epilepsy patients. We construct networks that predict multi-channel LFPs for 1, 10, and 100 milliseconds forward in time. Our results show that prediction using LSTM outperforms regression when predicting...
Critical ICU events like acute hypotension and septic shock are dangerous complications, leading to multiple organ failures and eventual death. Previously, pattern mining algorithms have been employed for extracting interesting rules in various clinical domains. However, the extracted rules are directly investigated by clinicians for diagnosing a disease. Towards this purpose, there is a need to develop...
Cardiac potassium (K+) channel plays an important role in cardiac electrical signaling. Mathematical models have been widely used to investigate the effects of K+ channels on cardiac functions. However, the model of K+ channel involves parametric uncertainties, which can be induced by fitting the model's parameters that best capture experimental data. Since the prediction of cardiac functions are...
Anti-cancer therapy efficacy in solid tumors mainly depends on drug transportation through the vasculature system and the extracellular matrix, on diffusion gradients and clonal heterogeneity within the tumor mass, as well as on the responses of the individual tumor cells to drugs and their interactions with each other and their local microenvironment. In this work, we develop a mathematical predictive...
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