Early hospital mortality prediction using vital signals

Early hospital mortality prediction is critical as intensivists strive to make efficient medical decisions about the severely ill patients staying in intensive care units. As a result, various methods have been developed to address this problem based on clinical records. However, some of the laboratory test results are time-consuming and need to be processed. In this paper, we propose a novel method to predict mortality using features extracted from the heart signals of patients within the first hour of ICU admission. In order to predict the risk, quantitative features have been computed based on the heart rate signals of ICU patients. Each signal is described in terms of 12 statistical and signal-based features. The extracted features are fed into eight classifiers: decision tree, linear discriminant, logistic regression, support vector machine (SVM), random forest, boosted trees, Gaussian SVM, and K-nearest neighborhood (K-NN). To derive insight into the performance of the proposed method, several experiments have been conducted using the well-known clinical dataset named Medical Information Mart for Intensive Care III (MIMIC-III). The experimental results demonstrate the capability of the proposed method in terms of precision, recall, F1-score, and area under the receiver operating characteristic curve (AUC). The decision tree classifier satisfies both accuracy and interpretability better than the other classifiers, producing an F1-score and AUC equal to 0.91 and 0.93, respectively. It indicates that heart rate signals can be used for predicting mortality in patients in the ICU, achieving a comparable performance with existing predictions that rely on high dimensional features from clinical records which need to be processed and may contain missing information.Comment: 11 pages, 5 figures, preprint of accepted paper in IEEE&ACM CHASE 2018 and published in Smart Health journa

An Approach for Deliberate Non-Compliance Detection during Opioid Abuse Surveillance by a Wearable Biosensor

Wearable sensors can be used to monitor opioid use and other key behaviors of interest, and to prompt interventions that promote behavioral change. The effectiveness of such systems is threatened by the potential of a subject\u27s deliberate non-compliance (DNC) to the monitoring. We define deliberate non-compliance as the process of giving one\u27s device to someone else when surveillance is on-going. The principal aim of this thesis is to develop an approach to leverage movement and cardiac features from a wearable sensor to detect such deliberate non-compliance by individuals under surveillance for opioid use. Data from 11 participants who presented to the Emergency Department following an opioid overdose was analyzed. Using a personalized machine learning classifier (model), we evaluated if a snippet of blood volume pulse (BVP) and accelerometer data received is coming from the expected participant or an alternate person. Analysis of our classier shows the viability of this approach, as we were able to detect DNC (or compliance) with over 90% accuracy within 3 seconds of its occurrence

Spiking-YOLO: Spiking Neural Network for Energy-Efficient Object Detection

Over the past decade, deep neural networks (DNNs) have demonstrated remarkable performance in a variety of applications. As we try to solve more advanced problems, increasing demands for computing and power resources has become inevitable. Spiking neural networks (SNNs) have attracted widespread interest as the third-generation of neural networks due to their event-driven and low-powered nature. SNNs, however, are difficult to train, mainly owing to their complex dynamics of neurons and non-differentiable spike operations. Furthermore, their applications have been limited to relatively simple tasks such as image classification. In this study, we investigate the performance degradation of SNNs in a more challenging regression problem (i.e., object detection). Through our in-depth analysis, we introduce two novel methods: channel-wise normalization and signed neuron with imbalanced threshold, both of which provide fast and accurate information transmission for deep SNNs. Consequently, we present a first spiked-based object detection model, called Spiking-YOLO. Our experiments show that Spiking-YOLO achieves remarkable results that are comparable (up to 98%) to those of Tiny YOLO on non-trivial datasets, PASCAL VOC and MS COCO. Furthermore, Spiking-YOLO on a neuromorphic chip consumes approximately 280 times less energy than Tiny YOLO and converges 2.3 to 4 times faster than previous SNN conversion methods.Comment: Accepted to AAAI 202

Designing energy-efficient computing systems using equalization and machine learning

As technology scaling slows down in the nanometer CMOS regime and mobile computing becomes more ubiquitous, designing energy-efficient hardware for mobile systems is becoming increasingly critical and challenging. Although various approaches like near-threshold computing (NTC), aggressive voltage scaling with shadow latches, etc. have been proposed to get the most out of limited battery life, there is still no “silver bullet” to increasing power-performance demands of the mobile systems. Moreover, given that a mobile system could operate in a variety of environmental conditions, like different temperatures, have varying performance requirements, etc., there is a growing need for designing tunable/reconfigurable systems in order to achieve energy-efficient operation. In this work we propose to address the energy- efficiency problem of mobile systems using two different approaches: circuit tunability and distributed adaptive algorithms. Inspired by the communication systems, we developed feedback equalization based digital logic that changes the threshold of its gates based on the input pattern. We showed that feedback equalization in static complementary CMOS logic enabled up to 20% reduction in energy dissipation while maintaining the performance metrics. We also achieved 30% reduction in energy dissipation for pass-transistor digital logic (PTL) with equalization while maintaining performance. In addition, we proposed a mechanism that leverages feedback equalization techniques to achieve near optimal operation of static complementary CMOS logic blocks over the entire voltage range from near threshold supply voltage to nominal supply voltage. Using energy-delay product (EDP) as a metric we analyzed the use of the feedback equalizer as part of various sequential computational blocks. Our analysis shows that for near-threshold voltage operation, when equalization was used, we can improve the operating frequency by up to 30%, while the energy increase was less than 15%, with an overall EDP reduction of ≈10%. We also observe an EDP reduction of close to 5% across entire above-threshold voltage range. On the distributed adaptive algorithm front, we explored energy-efficient hardware implementation of machine learning algorithms. We proposed an adaptive classifier that leverages the wide variability in data complexity to enable energy-efficient data classification operations for mobile systems. Our approach takes advantage of varying classification hardness across data to dynamically allocate resources and improve energy efficiency. On average, our adaptive classifier is ≈100× more energy efficient but has ≈1% higher error rate than a complex radial basis function classifier and is ≈10× less energy efficient but has ≈40% lower error rate than a simple linear classifier across a wide range of classification data sets. We also developed a field of groves (FoG) implementation of random forests (RF) that achieves an accuracy comparable to Convolutional Neural Networks (CNN) and Support Vector Machines (SVM) under tight energy budgets. The FoG architecture takes advantage of the fact that in random forests a small portion of the weak classifiers (decision trees) might be sufficient to achieve high statistical performance. By dividing the random forest into smaller forests (Groves), and conditionally executing the rest of the forest, FoG is able to achieve much higher energy efficiency levels for comparable error rates. We also take advantage of the distributed nature of the FoG to achieve high level of parallelism. Our evaluation shows that at maximum achievable accuracies FoG consumes ≈1.48×, ≈24×, ≈2.5×, and ≈34.7× lower energy per classification compared to conventional RF, SVM-RBF , Multi-Layer Perceptron Network (MLP), and CNN, respectively. FoG is 6.5× less energy efficient than SVM-LR, but achieves 18% higher accuracy on average across all considered datasets

The same analysis approach: Practical protection against the pitfalls of novel neuroimaging analysis methods

Standard neuroimaging data analysis based on traditional principles of experimental design, modelling, and statistical inference is increasingly complemented by novel analysis methods, driven e.g. by machine learning methods. While these novel approaches provide new insights into neuroimaging data, they often have unexpected properties, generating a growing literature on possible pitfalls. We propose to meet this challenge by adopting a habit of systematic testing of experimental design, analysis procedures, and statistical inference. Specifically, we suggest to apply the analysis method used for experimental data also to aspects of the experimental design, simulated confounds, simulated null data, and control data. We stress the importance of keeping the analysis method the same in main and test analyses, because only this way possible confounds and unexpected properties can be reliably detected and avoided. We describe and discuss this Same Analysis Approach in detail, and demonstrate it in two worked examples using multivariate decoding. With these examples, we reveal two sources of error: A mismatch between counterbalancing (crossover designs) and cross-validation which leads to systematic below-chance accuracies, and linear decoding of a nonlinear effect, a difference in variance

Semi-Automatization of Support Vector Machines to Map Lithium (Li) Bearing Pegmatites

Machine learning (ML) algorithms have shown great performance in geological remote sensing applications. The study area of this work was the Fregeneda–Almendra region (Spain–Portugal) where the support vector machine (SVM) was employed. Lithium (Li)-pegmatite exploration using satellite data presents some challenges since pegmatites are, by nature, small, narrow bodies. Consequently, the following objectives were defined: (i) train several SVM’s on Sentinel-2 images with different parameters to find the optimal model; (ii) assess the impact of imbalanced data; (iii) develop a successful methodological approach to delineate target areas for Li-exploration. Parameter optimization and model evaluation was accomplished by a two-staged grid-search with cross-validation. Several new methodological advances were proposed, including a region of interest (ROI)-based splitting strategy to create the training and test subsets, a semi-automatization of the classification process, and the application of a more innovative and adequate metric score to choose the best model. The proposed methodology obtained good results, identifying known Li-pegmatite occurrences as well as other target areas for Li-exploration. Also, the results showed that the class imbalance had a negative impact on the SVM performance since known Li-pegmatite occurrences were not identified. The potentials and limitations of the methodology proposed are highlighted and its applicability to other case studies is discussed.The authors would like to thank the financial support provided by FCT—Fundação para a Ciência e a Tecnologia, I.P., with the ERA-MIN/0001/2017—LIGHTS project. The work was also supported by National Funds through the FCT project UIDB/04683/2020—ICT (Institute of Earth Sciences). Joana Cardoso-Fernandes is financially supported within the compass of a Ph.D. Thesis, ref. SFRH/BD/136108/2018, by national funds from MCTES through FCT, and co-financed by the European Social Fund (ESF) through POCH—Programa Operacional Capital Humano. The Spanish Ministerio de Ciencia, Innovacion y Universidades (Project RTI2018-094097-B-100, with ERDF funds) and the University of the Basque Country (UPV/EHU) (grant GIU18/084) also contributed economically

On-the-fly synthesizer programming with rule learning

This manuscript explores automatic programming of sound synthesis algorithms within the context of the performative artistic practice known as live coding. Writing source code in an improvised way to create music or visuals became an instrument the moment affordable computers were able to perform real-time sound synthesis with languages that keep their interpreter running. Ever since, live coding has dealt with real time programming of synthesis algorithms. For that purpose, one possibility is an algorithm that automatically creates variations out of a few presets selected by the user. However, the need for real-time feedback and the small size of the data sets (which can even be collected mid-performance) are constraints that make existing automatic sound synthesizer programmers and learning algorithms unfeasible. Also, the design of such algorithms is not oriented to create variations of a sound but rather to find the synthesizer parameters that match a given one. Other approaches create representations of the space of possible sounds, allowing the user to explore it by means of interactive evolution. Even though these systems are exploratory-oriented, they require longer run-times. This thesis investigates inductive rule learning for on-the-fly synthesizer programming. This approach is conceptually different from those found in both synthesizer programming and live coding literature. Rule models offer interpretability and allow working with the parameter values of the synthesis algorithms (even with symbolic data), making preprocessing unnecessary. RuLer, the proposed learning algorithm, receives a dataset containing user labeled combinations of parameter values of a synthesis algorithm. Among those combinations sharing the same label, it analyses the patterns based on dissimilarity. These patterns are described as an IF-THEN rule model. The algorithm parameters provide control to define what is considered a pattern. As patterns are the base for inducting new parameter settings, the algorithm parameters control the degree of consistency of the inducted settings respect to the original input data. An algorithm (named FuzzyRuLer) able to extend IF-THEN rules to hyperrectangles, which in turn are used as the cores of membership functions, is presented. The resulting fuzzy rule model creates a map of the entire input feature space. For such a pursuit, the algorithm generalizes the logical rules solving the contradictions by following a maximum volume heuristics. Across the manuscript it is discussed how, when machine learning algorithms are used as creative tools, glitches, errors or inaccuracies produced by the resulting models are sometimes desirable as they might offer novel, unpredictable results. The evaluation of the algorithms follows two paths. The first focuses on user tests. The second responds to the fact that this work was carried out within the computer science department and is intended to provide a broader, nonspecific domain evaluation of the algorithms performance using extrinsic benchmarks (i.e not belonging to a synthesizer's domain) for cross validation and minority oversampling. In oversampling tasks, using imbalanced datasets, the algorithm yields state-of-the-art results. Moreover, the synthetic points produced are significantly different from those created by the other algorithms and perform (controlled) exploration of more distant regions. Finally, accompanying the research, various performances, concerts and an album were produced with the algorithms and examples of this thesis. The reviews received and collections where the album has been featured show a positive reception within the community. Together, these evaluations suggest that rule learning is both an effective method and a promising path for further research.Aquest manuscrit explora la programació automàtica d’algorismes de síntesi de so dins del context de la pràctica artística performativa coneguda com a live coding. L'escriptura improvisada de codi font per crear música o visuals es va convertir en un instrument en el moment en què els ordinadors van poder realitzar síntesis de so en temps real amb llenguatges que mantenien el seu intèrpret en funcionament. D'aleshores ençà, el live coding comporta la programació en temps real d’algorismes de síntesi de so. Per a aquest propòsit, una possibilitat és tenir un algorisme que creï automàticament variacions a partir d'alguns presets seleccionats. No obstant, la necessitat de retroalimentació en temps real i la petita mida dels conjunts de dades són restriccions que fan que els programadors automàtics de sintetitzadors de so i els algorismes d’aprenentatge no siguin factibles d’utilitzar. A més, el seu disseny no està orientat a crear variacions d'un so, sinó a trobar els paràmetres del sintetitzador que aplicats a l'algorisme de síntesi produeixen un so determinat (target). Altres enfocaments creen representacions de l'espai de sons possibles, per permetre a l'usuari explorar-lo mitjançant l'evolució interactiva, però requereixen temps més llargs. Aquesta tesi investiga l'aprenentatge inductiu de regles per a la programació on-the-fly de sintetitzadors. Aquest enfocament és conceptualment diferent dels que es troben a la literatura. Els models de regles ofereixen interpretabilitat i permeten treballar amb els valors dels paràmetres dels algorismes de síntesi, sense processament previ. RuLer, l'algorisme d'aprenentatge proposat, rep dades amb combinacions etiquetades per l'usuari dels valors dels paràmetres d'un algorisme de síntesi. A continuació, analitza els patrons, basats en la dissimilitud, entre les combinacions de cada etiqueta. Aquests patrons es descriuen com un model de regles IF-THEN. Els paràmetres de l'algorisme proporcionen control per definir el que es considera un patró. Llavors, controlen el grau de consistència dels nous paràmetres de síntesi induïts respecte a les dades d'entrada originals. A continuació, es presenta un algorisme (FuzzyRuLer) capaç d’estendre les regles IF-THEN a hiperrectangles, que al seu torn s’utilitzen com a nuclis de funcions de pertinença. El model de regles difuses resultant crea un mapa complet de l'espai de la funció d'entrada. Per això, l'algorisme generalitza les regles lògiques seguint una heurística de volum màxim. Al llarg del manuscrit es discuteix com, quan s’utilitzen algorismes d’aprenentatge automàtic com a eines creatives, de vegades són desitjables glitches, errors o imprecisions produïdes pels models resultants, ja que poden oferir nous resultats imprevisibles. L'avaluació dels algorismes segueix dos camins. El primer es centra en proves d'usuari. El segon, que respon al fet que aquest treball es va dur a terme dins del departament de ciències de la computació, pretén proporcionar una avaluació més àmplia, no específica d'un domini, del rendiment dels algorismes mitjançant benchmarks extrínsecs utilitzats per cross-validation i minority oversampling. En tasques d'oversampling, mitjançant imbalanced data sets, l'algorisme proporciona resultats equiparables als de l'estat de l'art. A més, els punts sintètics produïts són significativament diferents als creats pels altres algorismes i realitzen exploracions (controlades) de regions més llunyanesEste manuscrito explora la programación automática de algoritmos de síntesis de sonido dentro del contexto de la práctica artística performativa conocida como live coding. La escritura de código fuente de forma improvisada para crear música o imágenes, se convirtió en un instrumento en el momento en que las computadoras asequibles pudieron realizar síntesis de sonido en tiempo real con lenguajes que mantuvieron su interprete en funcionamiento. Desde entonces, el live coding ha implicado la programación en tiempo real de algoritmos de síntesis. Para ese propósito, una posibilidad es tener un algoritmo que cree automáticamente variaciones a partir de unos pocos presets seleccionados. Sin embargo, la necesidad de retroalimentación en tiempo real y el pequeño tamaño de los conjuntos de datos (que incluso pueden recopilarse durante la misma actuación), limitan el uso de los algoritmos existentes, tanto de programación automática de sintetizadores como de aprendizaje de máquina. Además, el diseño de dichos algoritmos no está orientado a crear variaciones de un sonido, sino a encontrar los parámetros del sintetizador que coincidan con un sonido dado. Otros enfoques crean representaciones del espacio de posibles sonidos, para permitir al usuario explorarlo mediante evolución interactiva. Aunque estos sistemas están orientados a la exploración, requieren tiempos más largos. Esta tesis investiga el aprendizaje inductivo de reglas para la programación de sintetizadores on-the-fly. Este enfoque es conceptualmente diferente de los que se encuentran en la literatura, tanto de programación de sintetizadores como de live coding. Los modelos de reglas ofrecen interpretabilidad y permiten trabajar con los valores de los parámetros de los algoritmos de síntesis (incluso con datos simbólicos), haciendo innecesario el preprocesamiento. RuLer, el algoritmo de aprendizaje propuesto, recibe un conjunto de datos que contiene combinaciones, etiquetadas por el usuario, de valores de parámetros de un algoritmo de síntesis. Luego, analiza los patrones, en función de la disimilitud, entre las combinaciones de cada etiqueta. Estos patrones se describen como un modelo de reglas lógicas IF-THEN. Los parámetros del algoritmo proporcionan el control para definir qué se considera un patrón. Como los patrones son la base para inducir nuevas configuraciones de parámetros, los parámetros del algoritmo controlan también el grado de consistencia de las configuraciones inducidas con respecto a los datos de entrada originales. Luego, se presenta un algoritmo (llamado FuzzyRuLer) capaz de extender las reglas lógicas tipo IF-THEN a hiperrectángulos, que a su vez se utilizan como núcleos de funciones de pertenencia. El modelo de reglas difusas resultante crea un mapa completo del espacio de las clases de entrada. Para tal fin, el algoritmo generaliza las reglas lógicas resolviendo las contradicciones utilizando una heurística de máximo volumen. A lo largo del manuscrito se analiza cómo, cuando los algoritmos de aprendizaje automático se utilizan como herramientas creativas, los glitches, errores o inexactitudes producidas por los modelos resultantes son a veces deseables, ya que pueden ofrecer resultados novedosos e impredecibles. La evaluación de los algoritmos sigue dos caminos. El primero se centra en pruebas de usuario. El segundo, responde al hecho de que este trabajo se llevó a cabo dentro del departamento de ciencias de la computación y está destinado a proporcionar una evaluación más amplia, no de dominio específica, del rendimiento de los algoritmos utilizando beanchmarks extrínsecos para cross-validation y oversampling. En estas últimas pruebas, utilizando conjuntos de datos no balanceados, el algoritmo produce resultados equiparables a los del estado del arte. Además, los puntos sintéticos producidos son significativamente diferentes de los creados por los otros algoritmos y realizan una exploración (controlada) de regiones más distantes. Finalmente, acompañando la investigación, realicé diversas presentaciones, conciertos y un ´álbum utilizando los algoritmos y ejemplos de esta tesis. Las críticas recibidas y las listas donde se ha presentado el álbum muestran una recepción positiva de la comunidad. En conjunto, estas evaluaciones sugieren que el aprendizaje de reglas es al mismo tiempo un método eficaz y un camino prometedor para futuras investigaciones.Postprint (published version

Knowledge-Based Analysis of Genomic Expression Data by Using Different Machine Learning Algorithms for the Purpose of Diagnostic, Prognostic or Therapeutic Application

With more and more biological information generated, the most pressing task of bioinformatics has become to analyze and interpret various types of data, including nucleotide and amino acid sequences, protein structures, gene expression profiling and so on. In this dissertation, we apply the data mining techniques of feature generation, feature selection, and feature integration with learning algorithms to tackle the problems of disease phenotype classification, clinical outcome and patient survival prediction from gene expression profiles. We analyzed the effect of batch noise in microarray data on the performance of classification. Batchmatch, a batch adjusting algorithm based on double scaling method is advantageous over Combat, another batch correcting algorithm based on the empirical bayes frame work. In order to identify genes associated with disease phenotype classification or patient survival prediction from gene expression data, we compared and analyzed the performance of five feature selection algorithms. Our observations from these studies indicated that Gainratio algorithm performs better and more consistently over the other algorithms studied. When it comes to performance metric to choose the best classifiers, MCC gives unbiased performance results over accuracy in some endpoints, where class imbalance is more. In the aspect of classification algorithms, no single algorithm is absolutely superior to all others, though SVM achieved fairly good results in most endpoints. Naive bayes algorithm also performed well in some endpoints. Overall, from the total 65 models we reported (5 top models for 13 end points) SVM and SMO (a variant of SVM) dominate mostly, also the linear kernel performed well over RBF in our binary classifications