30 research outputs found

    Knowledge-based Biomedical Data Science 2019

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    Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table

    UNDERSTANDING WHOLE BRAIN ACTIVITY THROUGH BRAIN NETWORK MODELS

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    The central nervous system coordinates many neural subpopulations connected via macroscale white matter architecture and surface cortical connections to produce complex behavior depending on environmental cues. The activity occurs over different scales, from information transfer between individual neurons at the synapse level, to macroscale coordination of neural populations used to maximize information transfer between specialized brain regions. The whole brain activity measured through functional Magnetic Resonance Imaging (fMRI), allows us to observe how these large neural populations interact over time. Researchers have developed a set of Brain Network Models (BNMs), that simulate brain activity using the macroscale structure and different mathematical models to represent populational neural activity. These simulations have been able to reproduce properties of fMRI especially those averaged over long periods of time. These models represent a step towards an individualized model of brain activity, which is of clinical interest, as they can be constructed from individual estimates of the structural network. To find a good BNM to fit the individual fMRI data, however, is a difficult problem as BNMs represent a large family of mathematical models. Moreover, a large set of BNMs have reproduced time averaged metrics that have been used so far to compare the models with the fMRI data. In this thesis, we extend previous work on BNM research by establishing new dynamic metrics that would allow us to better differentiate between BNMs simulations on how well they reproduce measured fMRI dynamics (Chapter 2). In Chapter 3, we directly compare transient short-term trajectories by synchronizing the outputs of a BNM in relation to observed fMRI timeseries using a novel Machine Learning Algorithm, Neural Ordinary Differential Equations (ODE). Finally, we show that the Neural ODE can be used as its own stand-alone generative model and is able to simulate more realistic fMRI signals as they are able to reproduce complex metrics that previous models have not been able to recapitulate (Chapter 4). In short, we demonstrate that we have made progress in developing and quantifying BNMs and advanced the research of more realistic whole brain simulations.Ph.D

    Analysis and automatic identification of spontaneous emotions in speech from human-human and human-machine communication

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    383 p.This research mainly focuses on improving our understanding of human-human and human-machineinteractions by analysing paricipantsÂż emotional status. For this purpose, we have developed andenhanced Speech Emotion Recognition (SER) systems for both interactions in real-life scenarios,explicitly emphasising the Spanish language. In this framework, we have conducted an in-depth analysisof how humans express emotions using speech when communicating with other persons or machines inactual situations. Thus, we have analysed and studied the way in which emotional information isexpressed in a variety of true-to-life environments, which is a crucial aspect for the development of SERsystems. This study aimed to comprehensively understand the challenge we wanted to address:identifying emotional information on speech using machine learning technologies. Neural networks havebeen demonstrated to be adequate tools for identifying events in speech and language. Most of themaimed to make local comparisons between some specific aspects; thus, the experimental conditions weretailored to each particular analysis. The experiments across different articles (from P1 to P19) are hardlycomparable due to our continuous learning of dealing with the difficult task of identifying emotions inspeech. In order to make a fair comparison, additional unpublished results are presented in the Appendix.These experiments were carried out under identical and rigorous conditions. This general comparisonoffers an overview of the advantages and disadvantages of the different methodologies for the automaticrecognition of emotions in speech

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    Front-Line Physicians' Satisfaction with Information Systems in Hospitals

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    Day-to-day operations management in hospital units is difficult due to continuously varying situations, several actors involved and a vast number of information systems in use. The aim of this study was to describe front-line physicians' satisfaction with existing information systems needed to support the day-to-day operations management in hospitals. A cross-sectional survey was used and data chosen with stratified random sampling were collected in nine hospitals. Data were analyzed with descriptive and inferential statistical methods. The response rate was 65 % (n = 111). The physicians reported that information systems support their decision making to some extent, but they do not improve access to information nor are they tailored for physicians. The respondents also reported that they need to use several information systems to support decision making and that they would prefer one information system to access important information. Improved information access would better support physicians' decision making and has the potential to improve the quality of decisions and speed up the decision making process.Peer reviewe

    Automated Intelligent Cueing Device to Improve Ambient Gait Behaviors for Patients with Parkinson\u27s Disease

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    Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods to analyze gait signals collected through wearable sensors and accurately identify FoG episodes. We also investigated the potential of predicting the symptoms before their actual occurrence. We collected data from seven participants with PD using two Inertial Measurement Units (IMUs) on ankles. In our first study, we extracted engineered features from the signals and used machine learning (ML) methods to identify FoG episodes. We tested the performance of models using patient-dependent and patient-independent paradigms. The former models achieved 92.5% and 89.0% for average sensitivity and specificity, respectively. However, the conventional binary classification methods fail to accurately classify data if only data from normal gait periods are available. In order to identify FoG episodes in participants who did not freeze during data collection sessions, we developed a Deep Gait Anomaly Detector (DGAD) to identify anomalies (i.e., FoG) in the signals. DGAD was formed of convolutional layers and trained to automatically learn features from signals. The convolutional layers are followed by fully connected layers to reduce the dimensions of the features. A k-nearest neighbors (kNN) classifier is then used to classify the data as normal or FoG. The models identified 87.4% of FoG onsets, with 21.9% being predicted on average for each participant. This study demonstrates our algorithm\u27s potential for delivery of preventive cues. The DGAD algorithm was then implemented in an Android application to monitor gait patterns of PD patients in ambient environments. The phone triggered vibrotactile and auditory cues on a connected smartwatch if an FoG episode was identified. A 6-week in-home study showed the potentials for effective treatment of FoG severity in ambient environments using intelligent cueing devices
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