Data Science Commons^™

Named Entity Recognition From Biomedical Text, Maged Guirguis

Theses and Dissertations

As vast amounts of unstructured data are becoming available digitally, computer-based methods to extract relevant and meaningful information are needed. Named entity recognition (NER) is the task of identifying text spans that mention named entities, and to classify them into predefined categories. Despite the existence of numerous and well-versed NER methods, the bio-medical domain remains under-studied. The objective of this research is to identify an efficient technique for NER tasks from biomedical data. This is achieved by investigating using deep learning technologies namely pre-trained BERT [1] model and its variances SciBERT [2] and BioBERT [3]. Preprocessing the data before passing …

Multicollinearity Applied Stepwise Stochastic Imputation: A Large Dataset Imputation Through Correlation‑Based Regression, Benjamin D. Leiby, Darryl K. Ahner

Faculty Publications

This paper presents a stochastic imputation approach for large datasets using a correlation selection methodology when preferred commercial packages struggle to iterate due to numerical problems. A variable range-based guard rail modification is proposed that benefits the convergence rate of data elements while simultaneously providing increased confidence in the plausibility of the imputations. A large country conflict dataset motivates the search to impute missing values well over a common threshold of 20% missingness. The Multicollinearity Applied Stepwise Stochastic imputation methodology (MASS-impute) capitalizes on correlation between variables within the dataset and uses model residuals to estimate unknown values. Examination of the …

Automated Contouring And Planning In Radiation Therapy: What Is 'Clinically Acceptable'?, Hana Baroudi, Kristy K Brock, Wenhua Cao, Xinru Chen, Caroline Chung, Laurence E Court, Mohammad D El Basha, Maguy Farhat, Skylar Gay, Mary P Gronberg, Aashish Chandra Gupta, Soleil Hernandez, Kai Huang, David A Jaffray, Rebecca Lim, Barbara Marquez, Kelly Nealon, Tucker J Netherton, Callistus M Nguyen, Brandon Reber, Dong Joo Rhee, Ramon M Salazar, Mihir D Shanker, Carlos Sjogreen, Mckell Woodland, Jinzhong Yang, Cenji Yu, Yao Zhao

Faculty, Staff and Student Publications

Developers and users of artificial-intelligence-based tools for automatic contouring and treatment planning in radiotherapy are expected to assess clinical acceptability of these tools. However, what is 'clinical acceptability'? Quantitative and qualitative approaches have been used to assess this ill-defined concept, all of which have advantages and disadvantages or limitations. The approach chosen may depend on the goal of the study as well as on available resources. In this paper, we discuss various aspects of 'clinical acceptability' and how they can move us toward a standard for defining clinical acceptability of new autocontouring and planning tools.

Session11: Skip-Gcn : A Framework For Hierarchical Graph Representation Learning, Jackson Cates, Justin Lewis, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

Recently there has been high demand for the representation learning of graphs. Graphs are a complex data structure that contains both topology and features. There are first several domains for graphs, such as infectious disease contact tracing and social media network communications interactions. The literature describes several methods developed that work to represent nodes in an embedding space, allowing for classical techniques to perform node classification and prediction. One such method is the graph convolutional neural network that aggregates the node neighbor’s features to create the embedding. Another method, Walklets, takes advantage of the topological information stored in a graph …

2d Respiratory Sound Analysis To Detect Lung Abnormalities, Rafia Sharmin Alice, Kc Santosh

SDSU Data Science Symposium

Abstract. In this paper, we analyze deep visual features from 2D data representation(s) of the respiratory sound to detect evidence of lung abnormalities. The primary motivation behind this is that visual cues are more important in decision-making than raw data (lung sound). Early detection and prompt treatments are essential for any future possible respiratory disorders, and respiratory sound is proven to be one of the biomarkers. In contrast to state-of-the-art approaches, we aim at understanding/analyzing visual features using our Convolutional Neural Networks (CNN) tailored Deep Learning Models, where we consider all possible 2D data such as Spectrogram, Mel-frequency Cepstral Coefficients …

Temporal Tensor Factorization For Multidimensional Forecasting, Jackson Cates, Karissa Scipke, Randy Hoover, Kyle Caudle

SDSU Data Science Symposium

In the era of big data, there is a need for forecasting high-dimensional time series that might be incomplete, sparse, and/or nonstationary. The current research aims to solve this problem for two-dimensional data through a combination of temporal matrix factorization (TMF) and low-rank tensor factorization. From this method, we propose an expansion of TMF to two-dimensional data: temporal tensor factorization (TTF). The current research aims to interpolate missing values via low-rank tensor factorization, which produces a latent space of the original multilinear time series. We then can perform forecasting in the latent space. We present experimental results of the proposed …

Emotion Classification Of Indonesian Tweets Using Bidirectional Lstm, Aaron K. Glenn, Phillip M. Lacasse, Bruce A. Cox

Faculty Publications

Emotion classification can be a powerful tool to derive narratives from social media data. Traditional machine learning models that perform emotion classification on Indonesian Twitter data exist but rely on closed-source features. Recurrent neural networks can meet or exceed the performance of state-of-the-art traditional machine learning techniques using exclusively open-source data and models. Specifically, these results show that recurrent neural network variants can produce more than an 8% gain in accuracy in comparison with logistic regression and SVM techniques and a 15% gain over random forest when using FastText embeddings. This research found a statistical significance in the performance of …

Hemoglobin Concentration Impacts Viscoelastic Hemostatic Assays In Icu Admitted Patients, David J Roh, Tiffany R Chang, Aditya Kumar, Devin Burke, Glenda Torres, Katherine Xu, Winni Yang, Azzurra Cottarelli, Ernest Moore, Angela Sauaia, Kirk Hansen, Angela Velazquez, Amelia Boehme, Athina Vrosgou, Shivani Ghoshal, Soojin Park, Sachin Agarwal, Jan Claassen, E Sander Connolly, Gebhard Wagener, Richard O Francis, Eldad Hod

Faculty, Staff and Student Publications

Objectives: Low hemoglobin concentration impairs clinical hemostasis across several diseases. It is unclear whether hemoglobin impacts laboratory functional coagulation assessments. We evaluated the relationship of hemoglobin concentration on viscoelastic hemostatic assays in intracerebral hemorrhage (ICH) and perioperative patients admitted to an ICU.

Design: Observational cohort study and separate in vitro laboratory study.

Setting: Multicenter tertiary referral ICUs.

Patients: Two acute ICH cohorts receiving distinct testing modalities: rotational thromboelastometry (ROTEM) and thromboelastography (TEG), and a third surgical ICU cohort receiving ROTEM were evaluated to assess the generalizability of findings across disease processes and testing platforms. A separate in vitro ROTEM laboratory …

Biological Correlates Of The Effects Of Auricular Point Acupressure On Pain, Chao Hsing Yeh, Nada Lukkahatai, Xinran Huang, Hulin Wu, Hongyu Wang, Jingyu Zhang, Xinyi Sun, Thomas J Smith

Faculty, Staff and Student Publications

BACKGROUND: To identify candidate inflammatory biomarkers for the underlying mechanism of auricular point acupressure (APA) on pain relief and examine the correlations among pain intensity, interference, and inflammatory biomarkers.

DESIGN: This is a secondary data analysis.

METHODS: Data on inflammatory biomarkers collected via blood samples and patient self-reported pain intensity and interference from three pilot studies (chronic low back pain, n = 61; arthralgia related to aromatase inhibitors, n = 20; and chemotherapy-induced neuropathy, n = 15) were integrated and analyzed. This paper reports the results based on within-subject treatment effects (change in scores from pre- to post-APA intervention) for …

Machine Learning And Acute Stroke Imaging, Sunil A Sheth, Luca Giancardo, Marco Colasurdo, Visish M Srinivasan, Arash Niktabe, Peter Kan

Faculty, Staff and Student Publications

BACKGROUND: In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.

OBJECTIVE: To provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.

METHODS: In this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. …

Alphaviruses Detected In Mosquitoes In The North-Eastern Regions Of South Africa, 2014 To 2018, Milehna M Guarido, Isabel Fourie, Kgothatso Meno, Adriano Mendes, Megan A Riddin, Caitlin Macintyre, Sontaga Manyana, Todd Johnson, Maarten Schrama, Erin E Gorsich, Basil D Brooke, Antonio Paulo G Almeida, Marietjie Venter

Faculty, Staff and Student Publications

The prevalence and distribution of African alphaviruses such as chikungunya have increased in recent years. Therefore, a better understanding of the local distribution of alphaviruses in vectors across the African continent is important. Here, entomological surveillance was performed from 2014 to 2018 at selected sites in north-eastern parts of South Africa where alphaviruses have been identified during outbreaks in humans and animals in the past. Mosquitoes were collected using a net, CDC-light, and BG-traps. An alphavirus genus-specific nested RT-PCR was used for screening, and positive pools were confirmed by sequencing and phylogenetic analysis. We collected 64,603 mosquitoes from 11 genera, …

Social Impacts Of Robotics On The Labor And Employment Market, Kelvin Espinal

Dissertations, Theses, and Capstone Projects

Robotics have been introduced into the workplace to perform tasks that human beings have traditionally fulfilled. Complementing or substituting human labor with robotics eliminates human involvement in functions attributable to hazardous environments, heavy lifting, toxic substances, and repetitive low-level tasks. On the other hand, they are meant to be more efficient and cost-effective, saving money, time, and labor. However, since the introduction of robotics in the workforce, societal opposition has been towards this branch of technology in fear of losing employment, wages, and purpose.

Previous studies have reported an overarching societal fear that adopting robotics in the workplace and industry …

Analyzing Relationships With Machine Learning, Oscar Ko

Dissertations, Theses, and Capstone Projects

Procedurally, this project aims to take a dataset, analyze it, and offer insights to the audience in an easy-to-digest format. Conceptually, this project will seek to explore questions like: “Do couples that meet through online dating or dating apps have higher or lower quality relationships?”, “Can any features in this dataset help predict how a subject would rate their relationship quality?”, and “What other insights can I derive from using machine learning for exploratory analysis?” The intended audience for this project is anyone interested in romantic relationships or machine learning.

The dataset is from a Stanford University survey, “How Couples …

Revealing The Three-Dimensional Magnetic Texture With Machine Learning Models, Shihua Zhao

Dissertations, Theses, and Capstone Projects

Revealing three-dimensional (3D) magnetic textures with vector field electron tomography (VFET) is essential in studying novel magnetic materials with topologically protected spin textures potentially being used in the next-generation semiconductor industry. In this dissertation, we use machine learning (ML) models to reconstruct 3D magnetic textures from electron holography (EH) data.

We can feed the EH data, a series of two-dimensional (2D) phasemaps, into a neural network (NN) architecture directly or feed the EH data into a conventional VFET and then feed the reconstructed results into a NN. Thus, perceptive NN, either a simple convolutional neural network (CNN) or Unet architecture, …

Skeleton-Based Hand Gesture Recognition Using Data-Level Fusion, Oluwaleke Yusuf

Theses and Dissertations

Hand Gesture Recognition (HGR) is a form of perceptual computing that allows artificial systems to capture and interpret human gestures. HGR has applications in human-machine interaction, virtual reality, augmented reality, and human behavior analysis. The human hand can assume a near-infinite number of poses and orientations to form myriad gestures, thus increasing the difficulty of the HGR task.

The hand skeleton of connected joints effectively describes the hand’s geometric shape and thus contains richer semantic gesture information while eliminating noise from individual differences in physical hand characteristics. The efficacy and computational efficiency of skeleton-based HGR frameworks can be significantly enhanced …

Data Science Transfer Pathways From Associate's To Bachelor's Programs, Benjamin S. Baumer, Nicholas J. Horton

Statistical and Data Sciences: Faculty Publications

A substantial fraction of students who complete their college education at a public university in the United States begin their journey at one of the 935 public 2-year colleges. While the number of 4-year colleges offering bachelor’s degrees in data science continues to increase, data science instruction at many 2-year colleges lags behind. A major impediment is the relative paucity of introductory data science courses that serve multiple student audiences and can easily transfer. In addition, the lack of predefined transfer pathways (or articulation agreements) for data science creates a growing disconnect that leaves students who want to study data …

Determining The Proportionality Of Ischemic Stroke Risk Factors To Age, Elizabeth Hunter, John D. Kelleher

Articles

While age is an important risk factor, there are some disadvantages to including it in a stroke risk model: age can dominate the risk score and lead to over-or under-predictions in some age groups. There is evidence to suggest that some of these disadvantages are due to the non-proportionality of other risk factors with age, eg, risk factors contribute differently to stroke risk based on an individual’s age. In this paper, we present a framework to test if risk factors are proportional with age. We then apply the framework to a set of risk factors using Framingham heart study data …

Synthesize Heterogeneous Biological Knowledge Via Representation Learning For Alzheimer’S Disease Drug Repurposing, Kang-Lin Hsieh, German Plascencia-Villa, Ko-Hong Lin, George Perry, Xiaoqian Jiang, Yejin Kim

Faculty, Staff and Student Publications

Developing drugs for treating Alzheimer's disease has been extremely challenging and costly due to limited knowledge of underlying mechanisms and therapeutic targets. To address the challenge in AD drug development, we developed a multi-task deep learning pipeline that learns biological interactions and AD risk genes, then utilizes multi-level evidence on drug efficacy to identify repurposable drug candidates. Using the embedding derived from the model, we ranked drug candidates based on evidence from post-treatment transcriptomic patterns, efficacy in preclinical models, population-based treatment effects, and clinical trials. We mechanistically validated the top-ranked candidates in neuronal cells, identifying drug combinations with efficacy in …

A Bidirectional Deep Lstm Machine Learning Method For Flight Delay Modelling And Analysis, Desmond B. Bisandu, Irene Moulitsas

National Training Aircraft Symposium (NTAS)

Flight delays can be prevented by providing a reference point from an accurate prediction model because predicting flight delays is a problem with a specific space. Only a few algorithms consider predicted classes' mutual correlation during flight delay classification or prediction modelling tasks. None of these existing methods works for all scenarios. Therefore, the need to investigate the performance of more models in solving the problem of flight delay is vast and rapidly increasing. This paper presents the development and evaluation of LSTM and BiLSTM models by comparing them for a flight delay prediction. The LSTM does the feature extraction …

Integrated Organizational Machine Learning For Aviation Flight Data, Michael J. Pritchard, Paul Thomas, Eric Webb, Jon Martin, Austin Walden

National Training Aircraft Symposium (NTAS)

An increased availability of data and computing power has allowed organizations to apply machine learning techniques to various fleet monitoring activities. Additionally, our ability to acquire aircraft data has increased due to the miniaturization of small form factor computing machines. Aircraft data collection processes contain many data features in the form of multivariate time-series (continuous, discrete, categorical, etc.) which can be used to train machine learning models. Yet, three major challenges still face many flight organizations 1) integration and automation of data collection frameworks, 2) data cleanup and preparation, and 3) embedded machine learning framework. Data cleanup and preparation has …