EDMON - Electronic Disease Surveillance and Monitoring Network: A Personalized Health Model-based Digital Infectious Disease Detection Mechanism using Self-Recorded Data from People with Type 1 Diabetes

Through time, we as a society have been tested with infectious disease outbreaks of different magnitude, which often pose major public health challenges. To mitigate the challenges, research endeavors have been focused on early detection mechanisms through identifying potential data sources, mode of data collection and transmission, case and outbreak detection methods. Driven by the ubiquitous nature of smartphones and wearables, the current endeavor is targeted towards individualizing the surveillance effort through a personalized health model, where the case detection is realized by exploiting self-collected physiological data from wearables and smartphones. This dissertation aims to demonstrate the concept of a personalized health model as a case detector for outbreak detection by utilizing self-recorded data from people with type 1 diabetes. The results have shown that infection onset triggers substantial deviations, i.e. prolonged hyperglycemia regardless of higher insulin injections and fewer carbohydrate consumptions. Per the findings, key parameters such as blood glucose level, insulin, carbohydrate, and insulin-to-carbohydrate ratio are found to carry high discriminative power. A personalized health model devised based on a one-class classifier and unsupervised method using selected parameters achieved promising detection performance. Experimental results show the superior performance of the one-class classifier and, models such as one-class support vector machine, k-nearest neighbor and, k-means achieved better performance. Further, the result also revealed the effect of input parameters, data granularity, and sample sizes on model performances. The presented results have practical significance for understanding the effect of infection episodes amongst people with type 1 diabetes, and the potential of a personalized health model in outbreak detection settings. The added benefit of the personalized health model concept introduced in this dissertation lies in its usefulness beyond the surveillance purpose, i.e. to devise decision support tools and learning platforms for the patient to manage infection-induced crises

Hyperparameter selection of one-class support vector machine by self-adaptive data shifting

With flexible data description ability, one-class Support Vector Machine (OCSVM) is one of the most popular and widely-used methods for one-class classification (OCC). Nevertheless, the performance of OCSVM strongly relies on its hyperparameter selection, which is still a challenging open problem due to the absence of outlier data. This paper proposes a fully automatic OCSVM hyperparameter selection method, which requires no tuning of additional hyperparameter, based on a novel self-adaptive “data shifting” mechanism: Firstly, by efficient edge pattern detection (EPD) and “negatively” shifting edge patterns along the negative direction of estimated data density gradient, a constrained number of high-quality pseudo outliers are self-adaptively generated at more desirable locations, which readily avoids two major difficulties in previous outlier generation methods. Secondly, to avoid time-consuming cross-validation and enhance robustness to noise in the given training data, a pseudo target set is generated for model validation by “positively” shifting each given target datum along the positive direction of data density gradient. Experiments on synthetic and benchmark datasets demonstrate the effectiveness of the proposed method.This work was sponsored by the National Natural Science Foundation of China (Project no. 61170287, 61232016)

One-Class Subject Identification From Smartphone-Acquired Walking Data

In this work, a novel type of human identification system is proposed, which has the aim to recognize a user from his biometric traits of his way of walk. A smartphone is utilized to acquire motion data from the built-in sensors. Data from accelerometer and gyroscope are processed through a cycle extraction phase, a Convolutional Neural Network for feature extraction and a One-Class SVM classifier for identification. From quantitave results the system achieves an Equal Error Rate close to 1

Advanced anomaly detection algorithms based on virtual sensors and one-class techniques

La presente investigación aborda el análisis e implementación de sistemas de detección de anomalías basados en técnicas inteligentes. Concretamente, se lleva a cabo el estudio de dos de las estrategias más comúnmente empleadas. La primera consiste en el desarrollo de un sensor virtual a partir de un modelo híbrido e inteligente capaz de detectar situaciones anómalas. La segunda de las estrategias, se basa en el uso de técnicas \emph{one-class}, a partir de las cuales se implementan clasificadores capaces de determinar la aparición de anomalías en base al comportamiento esperado. Se realizan, por tanto, un análisis y una comparativa de ambas estrategias, poniendo de relieve el desempeño de cada una. Este trabajo, realizado de acuerdo a la modalidad de compendio de publicaciones, presenta un hilo conductor de acuerdo a la investigación efectuada, en el cual se reflejan el avance y las aportaciones sucesivas y concatenadas, con los tres artículos presentados. El primero de los trabajos, aborda la implementación de un sensor virtual, empleado para detectar anomalías en una máquina de obtención del material bicomponente, utilizado en la fabricación de palas de aerogenerador. En este caso, el sensor virtual se desarrolla a través de un modelo de regresión híbrido e inteligente. La aparición de desviaciones entre el valor predicho y real de la lectura del sensor, se presenta como criterio para detectar la anomalía. Esta aportación conlleva la necesidad de disponer de un usuario con cierto conocimiento acerca del umbral que determine la aparición de una anomalía. En consecuencia, en el segundo trabajo, se decide emplear sistemas inteligentes de tipo \emph{one-class}. Se propone la aplicación de este tipo de técnicas sobre una planta de laboratorio, cuyo objetivo es controlar el nivel de agua en un depósito, a la que se le provocan anomalías durante el correcto funcionamiento. Los resultados son altamente satisfactorios, consiguiendo que el sistema implementado detecte los fallos provocados sobre la planta. Como consecuencia del buen rendimiento de este tipo de técnicas en esta aportación, el tercero de los trabajos aborda, con ellas, la detección de fallos sobre la planta de mezclado de compuesto bicomponente del primero de los trabajos, cuya complejidad es notablemente superior. La aplicación de esta estrategia ofrece muy buenos resultados

Subspace Support Vector Data Description and Extensions

Machine learning deals with discovering the knowledge that governs the learning process. The science of machine learning helps create techniques that enhance the capabilities of a system through the use of data. Typical machine learning techniques identify or predict different patterns in the data. In classification tasks, a machine learning model is trained using some training data to identify the unknown function that maps the input data to the output labels. The classification task gets challenging if the data from some categories are either unavailable or so diverse that they cannot be modelled statistically. For example, to train a model for anomaly detection, it is usually challenging to collect anomalous data for training, but the normal data is available in abundance. In such cases, it is possible to use One-Class Classification (OCC) techniques where the model is trained by using data only from one class. OCC algorithms are practical in situations where it is vital to identify one of the categories, but the examples from that specific category are scarce. Numerous OCC techniques have been proposed in the literature that model the data in the given feature space; however, such data can be high-dimensional or may not provide discriminative information for classification. In order to avoid the curse of dimensionality, standard dimensionality reduction techniques are commonly used as a preprocessing step in many machine learning algorithms. Principal Component Analysis (PCA) is an example of a widely used algorithm to transform data into a subspace suitable for the task at hand while maintaining the meaningful features of a given dataset. This thesis provides a new paradigm that jointly optimizes a subspace and data description for one-class classification via Support Vector Data Description (SVDD). We initiated the idea of subspace learning for one class classification by proposing a novel Subspace Support Vector Data Description (SSVDD) method, which was further extended to Ellipsoidal Subspace Support Vector Data Description (ESSVDD). ESSVDD generalizes SSVDD for a hypersphere by using ellipsoidal data description and it converges faster than SSVDD. It is important to train a joint model for multimodal data when data is collected from multiple sources. Therefore, we also proposed a multimodal approach, namely Multimodal Subspace Support Vector Data Description (MSSVDD) for transforming the data from multiple modalities to a common shared space for OCC. An important contribution of this thesis is to provide a framework unifying the subspace learning methods for SVDD. The proposed Graph-Embedded Subspace Support Vector Data Description (GESSVDD) framework helps revealing novel insights into the previously proposed methods and allows deriving novel variants that incorporate different optimization goals. The main focus of the thesis is on generic novel methods which can be adapted to different application domains. We experimented with standard datasets from different domains such as robotics, healthcare, and economics and achieved better performance than competing methods in most of the cases. We also proposed a taxa identification framework for rare benthic macroinvertebrates. Benthic macroinvertebrate taxa distribution is typically very imbalanced. The amounts of training images for the rarest classes are too low for properly training deep learning-based methods, while these rarest classes can be central in biodiversity monitoring. We show that the classic one-class classifiers in general, and the proposed methods in particular, can enhance a deep neural network classification performance for imbalanced datasets