Several studies have made use of EEG features to detect specific mental health illnesses such as epilepsy or schizophrenia, as supplementary diagnosis to the usual symptom-based diagnoses. At the same time general mental health diagnostic tools (biomarker or symptom-based) to identify individuals who are manifesting early signs of mental health disorders are not commonly available. This thesis seeks to explore the potential use of EEG features as a biomarker-based tool for general mental health diagnosis. Specifically, the predictive ability using machine learning of a general biomarker derived from EEG readings elicited from an oddball auditory experiment to predict someone’s mental health status (mentally ill or healthy) is investigated in this study. Given that mindfulness exercises are regularly provided as treatment for a wide range of mental illnesses, the features of interest seek to quantify it as a measure of mental health. The 2 feature sets developed and tested in this study were collected from a traumatic brain injury (TBI) and healthy controls dataset. Further testing of these feature sets was done on the Bipolar and Schizophrenia Network on Intermediate Phenotypes (BSNIP) dataset containing multiple mental illnesses and healthy controls to test the features for generalizability. Feature Set 1 consisted of the average and variance of P300 and N200 ERP component peak amplitudes and latencies across the centroparietal and fronto-central EEG channels respectively. Feature Set 2 contains the average and variance of P300 and N200 ERP component mean amplitudes across the centro-parietal andfronto-central EEG channels respectively. The predictive ability of these 2 feature sets was tested. Logistic regression, support vector machines, decision trees, random forests, KNN classification algorithms were used, and random forest and KNN were used in combination with oversampling to predict the mental health status of the subjects (whether they were cases or healthy controls). The model performance was tested using accuracy, precision, sensitivity, specificity, f1 score, confusion matrices, and AUC of the ROC. The results of this thesis show promise on the use of EEG features as biomarkers to diagnose mental illnesses or to get a better understanding of mental wellness. The use of this technology opens doors for more accurate, biomarker-based diagnosis of mental health conditions, lowering the cost of mental health care, and making mental health care accessible for more people.

<p>    </p> <p>Machine learning (ML) models can help improve health care services. However, they need to be practical to gain wide adoption. A methodology is proposed in this study to evaluate the utility of different data modalities and cohort segmentation strategies when designing these models. The methodology is used to compare models that predict emergency department (ED) and inpatient hospital (IH) visits. The data modalities include socio-demographics, diagnosis and medications and cohort segmentation is based on age group and disease severity. The proposed methodology is applied to models developed using a cohort of insomnia patients and a cohort of general non- insomnia patients under different data modalities and segmentation strategies. All models are evaluated using the traditional intra-cohort testing. In addition, to establish the need for disease- specific segmentation, transfer testing is recommended where the same insomnia test patients used for intra-cohort testing are submitted to the general-patient model. The results indicate that using both diagnosis and medications as a source of data does not generally improve model performance and may increase its overhead. For insomnia patients, the best ED and IH models using both data modalities or either one of the modalities achieved an area under the receiver operating curve (AUC) of 0.71 and 78, respectively. Our results also show that an insomnia-specific model is not necessary when predicting future ED visits but may have merit when predicting IH visits. As such, we recommend the evaluation of disease-specific models using transfer testing. Based on these initial findings, a language model was pretrained using diagnosis codes. This model can be used for the prediction of future ED and IH visits for insomnia and non-insomnia patients. </p>

The goal of this thesis is to study the use of the Kantorovich-Rubinstein distance as to build a descriptor of sample complexity in classification problems. The idea is to use the fact that the Kantorovich-Rubinstein distance is a metric in the space of measures that also takes into account the geometry and topology of the underlying metric space. We associate to each class of points a measure and thus study the geometrical information that we can obtain from the Kantorovich-Rubinstein distance between those measures. We show that a large Kantorovich-Rubinstein distance between those measures allows to conclude that there exists a 1-Lipschitz classifier that classifies well the classes of points. We also discuss the limitation of the Kantorovich-Rubinstein distance as a descriptor.

With significant growth in modern forms of communication such as social media and micro- blogs we are able to gain a real-time understanding into events happening in many parts of the world. In addition, these modern forms of communication have helped shed light into the increasing instabilities across the world via the design of anticipatory intelligence systems [45, 43, 20] that can forecast population level events like civil unrest, disease occurrences with reasonable accuracy. Event forecasting systems are generally prone to become outdated (model drift) as they fail to keep-up with constantly changing patterns and thus require regular re-training in order to sustain their accuracy and reliability. In this dissertation we try to address some of the issues associated with design and maintenance of event forecasting systems in general. We propose and showcase performance results for a drift adaptation technique in event forecasting systems and also build a hybrid system for event coding which is cognizant of and seeks human intervention in uncertain prediction contexts to maintain a good balance between prediction-fidelity and cost of human effort. Specifically we identify several micro-tasks for event coding and build separate pipelines for each with uncertainty estimation capabilities and thereby be able to seek human feedback whenever required for each micro-task independent of the rest. / Doctor of Philosophy / Event forecasting systems help reduce violence, loss/damage to humans and property. They find applicability in supply chain management, prioritizing citizen grievances, designing mea- sures to control violence and minimize disruptions and also in applications like health/tourism by providing timely travel alerts. Several issues exist with the design and maintenance of such event forecasting systems in general. Predictions from such systems may drift away from ground reality over time if not adapted to various shifts (or changes) in event occurrence patterns in real-time. A continuous source of ground-truth events is of paramount necessity for the continuous maintenance of forecasting systems. However ground-truth events used for training may not be reliable but often information about their uncertainty is not reflected in the systems that are used to build the ground truth. This dissertation focuses on addressing such issues pertaining to design and maintenance of event forecasting systems. We propose a framework for online drift-adaptation and also build machine learning methods capable of modeling and capturing uncertainty in event detection systems. Finally we propose and built a hybrid event coding system that can capture the best of both automated and manual event coders. We breakdown the overall event coding pipeline into several micro-tasks and propose individual methods for each micro-task. Each method is built with the capability to know what it doesn't know and thus is capable of balancing quality vs throughput based on available human resources.

<p> In the field of Reliability, a new concept is introduced. The Combined Dependability theory is put forth in this thesis. </p> <p> Attempts are made to prove this theory experimentally by use of accelerated failure tests of GE-47 miniature lamps. The lamps are tested individually and ten in series. The individual lamp test results are then used for the prediction of the reliability of the ten lamps in series.</p> <p> The ten lamps in series simulate a machine with ten components. A failure of one of the components will produce a failure of the machine. The reliability of the machine can be found if the reliability of each part is known. The single part when tested individually must be subjected to the same stresses and conditions that it would encounter when operating in the machine. If this is not accomplished, the machine's calculation of reliability is invalid.</p> / Thesis / Master of Engineering (MEngr)

Robotic automation of CNC machines is becoming more popular as robot technology advances and becomes more readily available. While some CNC machines can run autonomously with part catchers, vertical milling centers require an external entity to keep the machine running. Collaborative and Industrial robots are the two main selections for automating a vertical CNC milling machine. We investigate specifically which robot type is most effective for machine tending a Haas VF2 vertical milling center. To do this a cell floor plan, risk assessment, overall equipment effectiveness evaluation, and a total cost analysis are performed to compare robots. With this results of each analysis process, it appears the industrial robot is most effective for the machine tending case.

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