Physical Sciences and Mathematics Commons^™

An Explainable And Statistically Validated Ensemble Clustering Model Applied To The Identification Of Traumatic Brain Injury Subgroups, Dacosta Yeboah, Louis Steinmeister, Daniel B. Hier, Bassam Hadi, Donald C. Wunsch, Gayla R. Olbricht, Tayo Obafemi-Ajayi

Electrical and Computer Engineering Faculty Research & Creative Works

We present a framework for an explainable and statistically validated ensemble clustering model applied to Traumatic Brain Injury (TBI). The objective of our analysis is to identify patient injury severity subgroups and key phenotypes that delineate these subgroups using varied clinical and computed tomography data. Explainable and statistically-validated models are essential because a data-driven identification of subgroups is an inherently multidisciplinary undertaking. In our case, this procedure yielded six distinct patient subgroups with respect to mechanism of injury, severity of presentation, anatomy, psychometric, and functional outcome. This framework for ensemble cluster analysis fully integrates statistical methods at several stages of …

Evaluation Of Standard And Semantically-Augmented Distance Metrics For Neurology Patients, Daniel B. Hier, Jonathan Kopel, Steven U. Brint, Donald C. Wunsch, Gayla R. Olbricht, Sima Azizi, Blaine Allen

Electrical and Computer Engineering Faculty Research & Creative Works

Background: Patient distances can be calculated based on signs and symptoms derived from an ontological hierarchy. There is controversy as to whether patient distance metrics that consider the semantic similarity between concepts can outperform standard patient distance metrics that are agnostic to concept similarity. The choice of distance metric can dominate the performance of classification or clustering algorithms. Our objective was to determine if semantically augmented distance metrics would outperform standard metrics on machine learning tasks.

Methods: We converted the neurological findings from 382 published neurology cases into sets of concepts with corresponding machine-readable codes. We calculated patient distances by …

Handling Missing Data For Unsupervised Learning With An Application On A Fitbir Traumatic Brain Injury (Tbi) Dataset, Louis Steinmeister, Dacosta Yeboah, Gayla Olbricht, Tayo Obafemi-Ajayi, Bassam Hadi, Daniel Hier, Donald C. Wunsch

Mathematics and Statistics Faculty Research & Creative Works

"The problem of missing data and imputation have been widely discussed amongst specialists. However, many data scientists and applied statisticians fail to appropriately consider this issue. Often, it seems intuitive to discard observations containing missing data or simply to substitute means. This can lead to disastrous consequences, particularly in an era of exponentially increasing data volumes. In the following, we show how inappropriate handling of missing data and an insufficient analysis of the censoring mechanism can lead to a bias, overconfidence in the estimation of parameters, could challenge the reproducibility of obtained results, and may distort the structure of the …

Fiber-Optic Micro-Probes For Measuring Acidity Level, Temperature, And Antigens, Yinfa Ma, Honglan Shi, Qingbo Yang, Hai Xiao

Chemistry Faculty Research & Creative Works

A pH micro-probe, a temperature micro-probe, and an immuno-based micro-probe each include a shaft for transmuting an input light signal and a tip for inserting into a cell or other substance for measuring pH, temperature, and/or antigens. The pH micro-probe and the temperature micro-probe each include a luminescent material positioned on the tip of the micro-probe. The light signal excites the luminescent material so that the luminescent material emits a luminescent light signal. The luminescent light signal has a property value dependent on the pH or temperature being measured and reflects back through the shaft for being measured by a …

System Efficient Esd Design Concept For Soft Failures, Giorgi Maghlakelidze

Doctoral Dissertations

"This research covers the topic of developing a systematic methodology of studying electrostatic discharge (ESD)-induced soft failures. ESD-induced soft failures (SF) are non-destructive disruptions of the functionality of an electronic system. The soft failure robustness of a USB3 Gen 1 interface is investigated, modeled, and improved. The injection is performed directly using transmission line pulser (TLP) with varying: pulse width, amplitude, polarity. Characterization provides data for failure thresholds and a SPICE circuit model that describes the transient voltage and current at the victim. Using the injected current, the likelihood of a SF is predicted. ESD protection by transient voltage suppressor …