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Articles 1 - 24 of 24
Full-Text Articles in Medicine and Health Sciences
Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava
Multi-Class Emotion Classification With Xgboost Model Using Wearable Eeg Headband Data, James Khamthung, Nibhrat Lohia, Seement Srivastava
SMU Data Science Review
Electroencephalography (EEG) or brainwave signals serve as a valuable source for discerning human activities, thoughts, and emotions. This study explores the efficacy of EXtreme Gradient Boosting (XGBoost) models in sentiment classification using EEG signals, specifically those captured by the MUSE EEG headband. The MUSE device, equipped with four EEG electrodes (TP9, AF7, AF8, TP10), offers a cost-effective alternative to traditional EEG setups, which often utilize over 60 channels in laboratory-grade settings. Leveraging a dataset from previous MUSE research (Bird, J. et al., 2019), emotional states (positive, neutral, and negative) were observed in a male and a female participant, each for …
Identifying Locations Of Drug Overdose In Las Vegas To Implement The Cardiff Violence Prevention Model, John Girard, Shikha Pandey, Zack Bunn, Chris Papesh, Jacquelyn Cheun Phd, Ying Zhang
Identifying Locations Of Drug Overdose In Las Vegas To Implement The Cardiff Violence Prevention Model, John Girard, Shikha Pandey, Zack Bunn, Chris Papesh, Jacquelyn Cheun Phd, Ying Zhang
SMU Data Science Review
This paper will provide an innovative approach to drug overdose prevention programs. Using data from Las Vegas emergency departments, this paper will analyze geospatial trends of drug overdoses. Leveraging the Cardiff Violence Prevention Model, the information is shared with local law enforcement agencies and decision makers to empower them to make evidence-based strategies. This paper highlights the efficacy of a data-driven model in addressing public health issues and underscoring its ability for even broader implementation in urban settings. Findings will suggest significant implications for policymaking, crime prevention, and public health initiatives, demonstrating a step towards a safer Las Vegas.
The Role Of Machine Learning In Improved Functionality Of Lower Limb Prostheses, Joaquin Dominguez, Richard Kim, Robert Slater
The Role Of Machine Learning In Improved Functionality Of Lower Limb Prostheses, Joaquin Dominguez, Richard Kim, Robert Slater
SMU Data Science Review
Lower-limb amputations can cause a plethora of obstacles that lead to a lower quality of life. Implementing machine learning techniques means advanced prosthetics can contribute to facilitating the lives of those that live with lower-limb amputations. Using the publicly available HuGaDB data set, the current study investigates several classification models (random forest, neural network, and Vowpal Wabbit) to predict the locomotive intentions of individuals using lower-limb prostheses. The results of this study show that the neural network model yielded the highest accuracy, comparable precision, and recall scores to the other models. However, the Vowpal Wabbit model's advantage in speed may …
Exploration Of Data Science Toolbox And Predictive Models To Detect And Prevent Medicare Fraud, Waste, And Abuse, Benjamin P. Goodwin, Adam Canton, Babatunde Olanipekun
Exploration Of Data Science Toolbox And Predictive Models To Detect And Prevent Medicare Fraud, Waste, And Abuse, Benjamin P. Goodwin, Adam Canton, Babatunde Olanipekun
SMU Data Science Review
The Federal Department of Health and Human Services spends approximately $830 Billion annually on Medicare of which an estimated $30 to $110 billion is some form of fraud, waste, or abuse (FWA). Despite the Federal Government’s ongoing auditing efforts, fraud, waste, and abuse is rampant and requires modern machine learning approaches to generalize and detect such patterns. New and novel machine learning algorithms offer hope to help detect fraud, waste, and abuse. The existence of publicly accessible datasets complied by The Centers for Medicare & Medicaid Services (CMS) contain vast quantities of structured data. This data, coupled with industry standardized …
Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti
Classification Of Breast Cancer Histopathological Images Using Semi-Supervised Gans, Balaji Avvaru, Nibhrat Lohia, Sowmya Mani, Vijayasrikanth Kaniti
SMU Data Science Review
Breast cancer is diagnosed more frequently than skin cancer in women in the United States. Most breast cancer cases are diagnosed in women, while children and men are less likely to develop the disease. Various tissues in the breast grow uncontrollably, resulting in breast cancer. Different treatments analyze microscopic histopathology images for diagnosis that help accurately detect cancer cells. Deep learning is one of the evolving techniques to classify images where accuracy depends on the volume and quality of labeled images. This study used various pre-trained models to train the histopathological images and analyze these models to create a new …
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
Predicting Insulin Pump Therapy Settings, Riccardo L. Ferraro, David Grijalva, Alex Trahan
SMU Data Science Review
Millions of people live with diabetes worldwide [7]. To mitigate some of the many symptoms associated with diabetes, an estimated 350,000 people in the United States rely on insulin pumps [17]. For many of these people, how effectively their insulin pump performs is the difference between sleeping through the night and a life threatening emergency treatment at a hospital. Three programmed insulin pump therapy settings governing effective insulin pump function are: Basal Rate (BR), Insulin Sensitivity Factor (ISF), and Carbohydrate Ratio (ICR). For many people using insulin pumps, these therapy settings are often not correct, given their physiological needs. While …
Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel
Cov-Inception: Covid-19 Detection Tool Using Chest X-Ray, Aswini Thota, Ololade Awodipe, Rashmi Patel
SMU Data Science Review
Since the pandemic started, researchers have been trying to find a way to detect COVID-19 which is a cost-effective, fast, and reliable way to keep the economy viable and running. This research details how chest X-ray radiography can be utilized to detect the infection. This can be for implementation in Airports, Schools, and places of business. Currently, Chest imaging is not a first-line test for COVID-19 due to low diagnostic accuracy and confounding with other viral pneumonia. Different pre-trained algorithms were fine-tuned and applied to the images to train the model and the best model obtained was fine-tuned InceptionV3 model …
Access Barriers To Telehealth, Quynh Chau, Amita Behuria Pathak, Daniel Turner, Jacquelyn Cheun, Carl Noe
Access Barriers To Telehealth, Quynh Chau, Amita Behuria Pathak, Daniel Turner, Jacquelyn Cheun, Carl Noe
SMU Data Science Review
Abstract. Telehealth has long been touted as an efficient and cost-effective way to deliver health care services to patients. The COVID-19 global pandemic hastened the adoption of this technology in the United States. Despite its promises, telehealth as a technology-based model of health service delivery has also highlighted access to care inequities in the form of uneven utilization across various patient demographics. This research uses machine learning and publicly available data sources to describe telehealth utilization based on social determinants of care. The implications of this application can be used to inform health care providers of how to target efforts …
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
Clinical Diagnosis Support With Convolutional Neural Network By Transfer Learning, Spencer Fogleman, Jeremy Otsap, Sangrae Cho
SMU Data Science Review
Breast cancer is prevalent among women in the United States. Breast cancer screening is standard but requires a radiologist to review screening images to make a diagnosis. Diagnosis through the traditional screening method of mammography currently has an accuracy of about 78% for women of all ages and demographics. A more recent and precise technique called Digital Breast Tomosynthesis (DBT) has shown to be more promising but is less well studied. A machine learning model trained on DBT images has the potential to increase the success of identifying breast cancer and reduce the time it takes to diagnose a patient, …
Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo
Machine Learning Approach To Distinguish Ulcerative Colitis And Crohn’S Disease Using Smote (Synthetic Minority Oversampling Technique) Methods, Kris Ghimire, Walter Lai, Yasser Omar, Thad Schwebke, Jamie Vo
SMU Data Science Review
Irritable Bowel Disease (IBD) affects a sizable portion of the US population, causing symptoms such as vomiting, abdominal pain, and diarrhea. Despite the disease’s prevalence, the precise cause is not fully understood. This study consists of endoscopic and histological data from patients diagnosed with IBD and a control population for reference. The machine learning models' focus is to classify patients into IBD types. Several models were analyzed, including decision trees, logistic regression, and k-nearest neighbors. In addition, various methods of SMOTE were applied to determine the most effective transformation and ensuring that the dataset is balanced. The best model with …
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova
Machine Learning In The Health Industry: Predicting Congestive Heart Failure And Impactors, Alexandra Norman, James Harding, Daria Zhukova
SMU Data Science Review
Cardiovascular diseases, Congestive Heart Failure in particular, are a leading cause of deaths worldwide. Congestive Heart Failure has high mortality and morbidity rates. The key to decreasing the morbidity and mortality rates associated with Congestive Heart Failure is determining a method to detect high-risk individuals prior to the development of this often-fatal disease. Providing high-risk individuals with advanced knowledge of risk factors that could potentially lead to Congestive Heart Failure, enhances the likelihood of preventing the disease through implementation of lifestyle changes for healthy living. When dealing with healthcare and patient data, there are restrictions that led to difficulties accessing …
Rule Out Screening For Undiagnosed Dementia And Alzheimer’S Disease Using An Ehr Based Machine Learning Solution, Branum Stephan, David A. Julovich, Dustin Bracy, Jeff Nguyen
Rule Out Screening For Undiagnosed Dementia And Alzheimer’S Disease Using An Ehr Based Machine Learning Solution, Branum Stephan, David A. Julovich, Dustin Bracy, Jeff Nguyen
SMU Data Science Review
Abstract. Current detection methods for Dementia and Alzheimer’s disease include cerebral spinal fluid (CSF) markers and/or the use of positron emission tomography (PET) imaging, both being high-cost, highly invasive testing methods. The need for low-cost, minimally invasive methods to prescreen individuals for cognitive impairment has been a challenge for many years. Today’s costs associated with an annual screen for all adults 65 and above using current methods (CSF, PET) reach well beyond trillions of dollars per year. Motivated by the limited accessibly and high costs, an alternative tool presented within this paper demonstrates an effective rule out screening for Dementia …
Using Ai To Diagnose Covid-19 From Patient Chest Ct Scans, Samuel Arellano, Liang Huang, Joe Jiang, Kenneth Richardson, Omar Yasser
Using Ai To Diagnose Covid-19 From Patient Chest Ct Scans, Samuel Arellano, Liang Huang, Joe Jiang, Kenneth Richardson, Omar Yasser
SMU Data Science Review
Rapid and accurate detection of COVID-19 remains the best weapon to control and prevent the spread of this pandemic, at least before a vaccine or treatment is available. In this study, we trained our custom computer vision models to predict COVID-19 from patients’ CT scans. We trained one model using Google’s AutoML Vision platform and achieved comparable accuracy with previously reported models. We also trained several custom models using transfer learning by taking advantage of several well-unknown pre-trained computer vision models, including Resnet and Inception models. The models are fine-tuned with a relatively large dataset and their high accuracy should …
Sars-Cov-2 Pandemic Analytical Overview With Machine Learning Predictability, Anthony Tanaydin, Jingchen Liang, Daniel W. Engels
Sars-Cov-2 Pandemic Analytical Overview With Machine Learning Predictability, Anthony Tanaydin, Jingchen Liang, Daniel W. Engels
SMU Data Science Review
Understanding diagnostic tests and examining important features of novel coronavirus (COVID-19) infection are essential steps for controlling the current pandemic of 2020. In this paper, we study the relationship between clinical diagnosis and analytical features of patient blood panels from the US, Mexico, and Brazil. Our analysis confirms that among adults, the risk of severe illness from COVID-19 increases with pre-existing conditions such as diabetes and immunosuppression. Although more than eight months into pandemic, more data have become available to indicate that more young adults were getting infected. In addition, we expand on the definition of COVID-19 test and discuss …
Fall Detection: Threshold Analysis Of Wrist-Worn Motion Sensor Signals, Michael J. Wolfe, Jospeh Caguioa, Andy Nguyen, Jacquelyn Cheun Phd
Fall Detection: Threshold Analysis Of Wrist-Worn Motion Sensor Signals, Michael J. Wolfe, Jospeh Caguioa, Andy Nguyen, Jacquelyn Cheun Phd
SMU Data Science Review
In this paper, we present a detection algorithm that accurately differentiates the event of a person falling from normal Activities of Daily Living (ADL). Our algorithm processes signals recorded from accelerometers and gyroscopes built into wearable activity monitoring devices such as smart watches that are worn on an individual’s wrist. Existing algorithms are accurate but imprecise, and rely too much on inconveniently-placed sensors. We propose a pipeline that improves precision without sacrificing accuracy and ease of use. We present the use of a combination of threshold-based and machine learning-based approaches to develop a refined fall-detection algorithm that builds upon previous …
A Novel Methodology To Identify The Primary Topics Contained Within The Covid-19 Research Corpus, Allen Crane, Brock Freidrich, William Fehlman, Igor Frolow, Daniel W. Engels
A Novel Methodology To Identify The Primary Topics Contained Within The Covid-19 Research Corpus, Allen Crane, Brock Freidrich, William Fehlman, Igor Frolow, Daniel W. Engels
SMU Data Science Review
In this paper, we present a novel framework and system for the identification of primary research topics from within a corpus of related publications, the classification of individual publications according to these topics, and the results of the application of our framework and system to the COVID-19 Open Research Dataset (CORD-19). CORD-19 is a corpus of published peer reviewed and pre-peer reviewed articles related to the coronavirus that causes COVID-19. Using machine learning techniques, such as Non-negative Matrix Factorization for Natural Language Processing and a Bayesian classifier, we developed a novel framework and system that automatically extracts sparse and meaningful …
Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre
Stationary Exercise Classification Using Imus And Deep Learning, Andrew M. Heroy, Zackary Gill, Samantha Sprague, David Stroud, John Santerre
SMU Data Science Review
In the current market, successful fitness tracking devices utilize heart rate and GPS to determine performance. These devices are useful, but don't extensively classify stationary exercise. This paper proposes a modern approach for tuning and investigating optimal neural network types on stationary exercises using Inertial Measurement Units (IMUs). Using three IMUs located on the ankle, waist, and wrist, data is collected to map the body as it moves during the stationary physical activity. A novel five-stage deep learning tuning system was written and deployed to classify user movement as one of three classes: air squats, jumping jacks, and kettlebell swings. …
Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas
Personalized Detection Of Anxiety Provoking News Events Using Semantic Network Analysis, Jacquelyn Cheun Phd, Luay Dajani, Quentin B. Thomas
SMU Data Science Review
In the age of hyper-connectivity, 24/7 news cycles, and instant news alerts via social media, mental health researchers don't have a way to automatically detect news content which is associated with triggering anxiety or depression in mental health patients. Using the Associated Press news wire, a semantic network was built with 1,056 news articles containing over 500,000 connections across multiple topics to provide a personalized algorithm which detects problematic news content for a given reader. We make use of Semantic Network Analysis to surface the relationship between news article text and anxiety in readers who struggle with mental health disorders. …
Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre
Predicting Premature Birth Risk With Cfrna, Jason Lin, Jonathan Marin, John Santerre
SMU Data Science Review
Identifying which genes are early indicators for preterm births using cell-free ribonucleic acid (cfRNA) from non-invasive blood tests provided by pregnant women can improve prenatal care. Currently, there are no medical tests for early detection of preterm birth risk in routine checkups for pregnant women. Recent studies have shown potential genes that can predict preterm birth. Machine learning techniques are utilized to see if the Area Under the Curve (AUC) can be improved upon when evaluating the prediction accuracy for chosen genes sequences and concentrations. Using cell-free RNA data from non-invasive blood tests in conjunction with machine learning, we improve …
Identifying High Risk Patients For Hospital Readmission, Ethan Graham, Asha Saxena, Heather Kirby
Identifying High Risk Patients For Hospital Readmission, Ethan Graham, Asha Saxena, Heather Kirby
SMU Data Science Review
The Affordable Care Act (ACA), passed in 2010, set forth a framework for healthcare providers to have a vested interest in better patient outcomes and to reduce the Total Cost of Care (TCOC) for patients. A large portion of TCOC comes from patients who make multiple unscheduled hospital visits for the same underlying pathology: a hospital readmission. In this paper, we tackle the difficulty of identifying risk markers for diabetes patients’ hospital readmission. Using data from the Health Facts Database, we use logistic regression and support vector machines to identify the risk that a diabetes patient has of a hospital …
The Simultaneous Detection And Classification Of Mass And Calcification Leading To Breast Cancer In Mammograms, Scott Gozdzialski, Alex Stern, Ireti Fasere, Daniel W. Engels
The Simultaneous Detection And Classification Of Mass And Calcification Leading To Breast Cancer In Mammograms, Scott Gozdzialski, Alex Stern, Ireti Fasere, Daniel W. Engels
SMU Data Science Review
In this paper, we present a novel method for detecting and classifying breast cancer calcification and masses in a single step. The detection and classification steps of calcifications and masses identifiable with a mammogram image are typically performed independently even though their simultaneous solution may lead to a more efficient approach. Our novel method utilizes a Convolutional Neural Network (CNN) to classify the calcifications and masses of different cropped images of a mammogram. We utilize a sliding window detector to break apart full mammogram images into sub-images, and identify and classify the observable objects in the sub-images. We receive multiple …
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
Overcoming Small Data Limitations In Heart Disease Prediction By Using Surrogate Data, Alfeo Sabay, Laurie Harris, Vivek Bejugama, Karen Jaceldo-Siegl
SMU Data Science Review
In this paper, we present a heart disease prediction use case showing how synthetic data can be used to address privacy concerns and overcome constraints inherent in small medical research data sets. While advanced machine learning algorithms, such as neural networks models, can be implemented to improve prediction accuracy, these require very large data sets which are often not available in medical or clinical research. We examine the use of surrogate data sets comprised of synthetic observations for modeling heart disease prediction. We generate surrogate data, based on the characteristics of original observations, and compare prediction accuracy results achieved from …
Evaluating Feasibility Of Blockchain Application For Dscsa Compliance, Tracie Scott, Armand L. Post, Johnny Quick, Sohail Rafiqi
Evaluating Feasibility Of Blockchain Application For Dscsa Compliance, Tracie Scott, Armand L. Post, Johnny Quick, Sohail Rafiqi
SMU Data Science Review
Abstract. We evaluated the feasibility of using a blockchain technology to create a traceability solution for pharmaceutical drugs that would promote compliance with recent legislation. Counterfeit and other illegitimate pharmaceutical drugs threaten patient safety, drug efficacy, and patient trust. The purpose of the Drug Supply Chain Security Act (DSCSA) is to greatly reduce distribution of illegitimate drugs by requiring all pharmaceuticals to be serialized and traceable from the manufacturer through the supply chain to the dispenser. A software application to serialize and track pharmaceuticals must overcome numerous obstacles. In particular, the solution must provide a high degree of trust while …
Open Cycle: Forecasting Ovulation For Family Planning, Karen Clark, Mridul Jain, Araya Messa, Vinh Le, Eric C. Larson
Open Cycle: Forecasting Ovulation For Family Planning, Karen Clark, Mridul Jain, Araya Messa, Vinh Le, Eric C. Larson
SMU Data Science Review
Abstract: Forecasting the length and different phases of a woman’s menstrual cycle, especially ovulation, is an important aspect of family planning. Predicting fertility has many uses in family planning including avoiding pregnancy and assisting couples in becoming pregnant. Past methods have focused on monitoring basal body temperature (BBT), cervical mucus changes, and hormonal levels to determine fertility. While these methods can provide an accurate prediction of ovulation these tests can become expensive, time-consuming, and do not provide prediction until after ovulation has occurred. In this paper, we compare conventional fertility assessment that is based on a rule known as “three-over-six” …