Engineering Commons^™

Communicating Uncertain Information From Deep Learning Models In Human Machine Teams, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank, Luke Andrews, Cihan H. Dagli

Engineering Management and Systems Engineering Faculty Research & Creative Works

The role of human-machine teams in society is increasing, as big data and computing power explode. One popular approach to AI is deep learning, which is useful for classification, feature identification, and predictive modeling. However, deep learning models often suffer from inadequate transparency and poor explainability. One aspect of human systems integration is the design of interfaces that support human decision-making. AI models have multiple types of uncertainty embedded, which may be difficult for users to understand. Humans that use these tools need to understand how much they should trust the AI. This study evaluates one simple approach for communicating …

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 …

Effect Of Subsurface Microseismicity On The Motion Of Surrounding Dispersed Droplets -- Data, Chao Zeng, Wen Deng

Effect of Subsurface Microseismicity on the Motion of Surrounding Dispersed Droplets – Data

Spreadsheet - Data plotted in Figure 4 and Figure 5

Supporting information

Effect Of Subsurface Microseismicity On The Motion Of Surrounding Dispersed Droplets: Supporting Information, Chao Zeng, Wen Deng

Research Data

The human-induced seismicity has called substantial attention in recent years. The effect of seismicity on the subsurface structure has been extensively studied. However, the effect of seismicity, especially those microseismicity, on surrounding immiscible fluids is rarely investigated. In porous media with two or more immiscible fluids, different amplitudes of vibration induced by seismicity have distinct effects on the dynamic behavior of fluids. Three types of pore-scale models are prevalent in the analysis of the motion of immiscible droplets. The underlying assumptions and accuracy of these models are compared in this study in both frequency domain and time domain. The frequency …