Medicine and Health Sciences Commons^™

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, …

Exploration Of Dark Chemical Genomics Space Via Portal Learning: Applied To Targeting The Undruggable Genome And Covid-19 Anti-Infective Polypharmacology, Tian Cai, Li Xie, Muge Chen, Yang Liu, Di He, Shuo Zhang, Cameron Mura, Philip Boume, Lei Xie

Publications and Research

Advances in biomedicine are largely fueled by exploring uncharted territories of human biology. Machine learning can both enable and accelerate discovery, but faces a fundamental hurdle when applied to unseen data with distributions that differ from previously observed ones—a common dilemma in scientific inquiry. We have developed a new deep learning framework, called Portal Learning , to explore dark chemical and biological space. Three key, novel components of our approach include: (i) end-to-end, step-wise transfer learning, in recognition of biology’s sequence-structure-function paradigm, (ii) out-of-cluster meta-learning, and (iii) stress model selection. Portal Learning provides a practical solution to the out-of-distribution (OOD) …

Determining States Of Movement In Humans Using Minimally Processed Eeg Signals And Various Classification Methods, Maurice Barnett

All Theses

Electroencephalography (EEG) is a non-invasive technique used in both clinical and research settings to record neuronal signaling in the brain. The location of an EEG signal as well as the frequencies at which its neuronal constituents fire correlate with behavioral tasks, including discrete states of motor activity. Due to the number of channels and fine temporal resolution of EEG, a dense, high-dimensional dataset is collected. Transcranial direct current stimulation (tDCS) is a treatment that has been suggested to improve motor functions of Parkinson’s disease and chronic stroke patients when stimulation occurs during a motor task. tDCS is commonly administered without …

Searching For Imaging Biomarkers Of Psychotic Dysconnectivity, Amanda L. Rodrigue, Dana Mastrovito, Oscar Esteban, Joke Durnez, Marinka M. G. Koenis, Ronald Janssen, Aaron Alexander-Bloch, Emma M. Knowles, Samuel R. Mathias, John Blangero

School of Medicine Publications and Presentations

Background: Progress in precision psychiatry is predicated on identifying reliable individual-level diagnostic biomarkers. For psychosis, measures of structural and functional connectivity could be promising biomarkers given consistent reports of dysconnectivity across psychotic disorders using magnetic resonance imaging.

Methods: We leveraged data from four independent cohorts of patients with psychosis and control subjects with observations from approximately 800 individuals. We used group-level analyses and two supervised machine learning algorithms (support vector machines and ridge regression) to test within-, between-, and across-sample classification performance of white matter and resting-state connectivity metrics.

Results: Although we replicated group-level differences in brain connectivity, individual-level classification …

Factors Influencing Intent To Take A Covid-19 Test In The United States, Sheila Rutto

Theses and Dissertations

In 2020, COVID-19 became the first pandemic in the world’s history that brought the entire world to an abrupt and unexpected halt. Since the first reported case of the disease to date, the novel coronavirus has been able to wreak havoc in literary every corner of the globe and left an ever-growing number of unprecedented fatalities. The normal way of life has been disrupted, and the level of uncertainty about the end of this pandemic continues to manifest to many. Due to the urgency to bring this pandemic under control, medical officers have been able to recommend actions that people …

Magnetic Resonance Imaging Sequence Identification Using A Metadata Learning Approach, Shuai Liang, Derek Beaton, Stephen R. Arnott, Tom Gee, Mojdeh Zamyadi, Robert Bartha, Sean Symons, Glenda M. Macqueen, Stefanie Hassel, Jason P. Lerch, Evdokia Anagnostou, Raymond W. Lam, Benicio N. Frey, Roumen Milev, Daniel J. Müller, Sidney H. Kennedy, Christopher J.M. Scott, Stephen C. Strother

Medical Biophysics Publications

Despite the wide application of the magnetic resonance imaging (MRI) technique, there are no widely used standards on naming and describing MRI sequences. The absence of consistent naming conventions presents a major challenge in automating image processing since most MRI software require a priori knowledge of the type of the MRI sequences to be processed. This issue becomes increasingly critical with the current efforts toward open-sharing of MRI data in the neuroscience community. This manuscript reports an MRI sequence detection method using imaging metadata and a supervised machine learning technique. Three datasets from the Brain Center for Ontario Data Exploration …

Exploratory Data Mining Techniques (Decision Tree Models) For Examining The Impact Of Internet-Based Cognitive Behavioral Therapy For Tinnitus: Machine Learning Approach, Hansapani Rodrigo, Eldré W. Beukes, Gerhard Andersson, Vinaya Manchaiah

School of Mathematical and Statistical Sciences Faculty Publications and Presentations

Background: There is huge variability in the way that individuals with tinnitus respond to interventions. These experiential variations, together with a range of associated etiologies, contribute to tinnitus being a highly heterogeneous condition. Despite this heterogeneity, a “one size fits all” approach is taken when making management recommendations. Although there are various management approaches, not all are equally effective. Psychological approaches such as cognitive behavioral therapy have the most evidence base. Managing tinnitus is challenging due to the significant variations in tinnitus experiences and treatment successes. Tailored interventions based on individual tinnitus profiles may improve outcomes. Predictive models of treatment …

Pairwise Correlation Analysis Of The Alzheimer’S Disease Neuroimaging Initiative (Adni) Dataset Reveals Significant Feature Correlation, Erik D. Huckvale, Matthew W. Hodgman, Brianna B. Greenwood, Devorah O. Stucki, Katrisa M. Ward, Mark T. W. Ebbert, John S. K. Kauwe, The Alzheimer’S Disease Neuroimaging Initiative, The Alzheimer’S Disease Metabolomics Consortium, Justin B. Miller

Sanders-Brown Center on Aging Faculty Publications

The Alzheimer’s Disease Neuroimaging Initiative (ADNI) contains extensive patient measurements (e.g., magnetic resonance imaging [MRI], biometrics, RNA expression, etc.) from Alzheimer’s disease (AD) cases and controls that have recently been used by machine learning algorithms to evaluate AD onset and progression. While using a variety of biomarkers is essential to AD research, highly correlated input features can significantly decrease machine learning model generalizability and performance. Additionally, redundant features unnecessarily increase computational time and resources necessary to train predictive models. Therefore, we used 49,288 biomarkers and 793,600 extracted MRI features to assess feature correlation within the ADNI dataset to determine the …

High-Dimensional Feature Selection And Multi-Level Causal Mediation Analysis With Applications To Human Aging And Cluster-Based Intervention Studies, Hachem Saddiki

Doctoral Dissertations

Many questions in public health and medicine are fundamentally causal in that our objective is to learn the effect of some exposure, randomized or not, on an outcome of interest. As a result, causal inference frameworks and methodologies have gained interest as a promising tool to reliably answer scientific questions. However, the tasks of identifying and efficiently estimating causal effects from observed data still pose significant challenges under complex data generating scenarios. We focus on (1) high-dimensional settings where the number of variables is orders of magnitude higher than the number of observations; and (2) multi-level settings, where study participants …

Supplementary Material For: Ex Vivo Thrombus Mr Imaging Features And Patient Clinical Data Enable Prediction Of Acute Ischemic Stroke Cause, Spencer D. Christiansen

Robarts Vascular Research Publications

SUPPLEMENTARY MATERIAL to:

Ex Vivo Thrombus Magnetic ResonanceImaging Features and Patient Clinical DataEnable Prediction of Acute Ischemic Stroke Cause https://doi.org/10.1161/SVIN.121.000157

Spencer D. Christiansen, PhD,1,2 Junmin Liu, PhD,1 Maria Bres Bullrich, MD,3 Manas Sharma, MD,4 Sachin K. Pandey, MD,4 Melfort Boulton, MD, PhD,3 Sebastian Fridman, MD, MPH,3 Luciano A. Sposato, MD, MBA,3 and Maria Drangova, PhD1,2

1 Robarts Research Institute, London, Ontario, Canada,

2 Department of Medical Biophysics, Western University, London, Ontario, Canada,

3 Department of Clinical Neurological Sciences, Western University, London, Ontario, Canada,

4 Department of Medical Imaging, Western University, London, Ontario, Canada

Machine Learning Guided Postnatal Gestational Age Assessment Using New-Born Screening Metabolomic Data In South Asia And Sub-Saharan Africa, Sunil Sazawal, Kelli K. Ryckman, Sayan Das, Muhammad Imran Nisar, Usma Mehmood, Amina Barkat, Farah Khalid, Muhammad Ilyas Muhammad Ilyas, Ambreen Nizar, Fyezah Jehan

Department of Paediatrics and Child Health

Background: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine …

Examining The Viability Of Computational Psychiatry: Approaches Into The Future, Mitchell Ostrow

The Yale Undergraduate Research Journal

As modern medicine becomes increasingly personalized, psychiatry lags behind, using poorly-understood drugs and therapies to treat mental disorders. With the advent of methods that capture large quantities of data, such as genome-wide analyses or fMRI, machine learning (ML) approaches have become prominent in neuroscience. This is promising for studying the brain’s function, but perhaps more importantly, these techniques can potentially predict the onset of disorder and treatment response. Experimental approaches that use naive machine learning algorithms have dominated research in computational psychiatry over the past decade. In a critical review and analysis, I argue that biologically realistic approaches will be …

Improving Animal Monitoring Using Small Unmanned Aircraft Systems (Suas) And Deep Learning Networks, Meilun Zhou, Jared A. Elmore, Sathishkumar Samiappan, Kristine O. Evans, Morgan Pfeiffer, Bradley F. Blackwell, Raymond B. Iglay

USDA Wildlife Services: Staff Publications

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with …

Validation Of A Single Channel Eeg For The Athlete: A Machine Learning Protocol To Accurately Detect Sleep Stages, Kayla Thompson, Kamil Celoch, Frankie Pizzo, Ana I. Fins, Jaime Tartar

Journal for Sports Neuroscience

There is a large and growing movement towards the use of wearable technologies for sleep assessment. This trend is largely due to the desire for comfortable, burden free, and inexpensive technology. In tandem, given the competitive nature of professional athletes enduring high training load, sleep is often jeopardized which can result in adverse outcomes. Wearable devices hold the promise of increasing the ease of monitoring sleep in athletes which can inform health and recovery status, as well as aid performance optimization. However, wearable devices typically lack sufficient validity to assess sleep – and especially sleep stages. To address this concern, …

Deep Learning Applications In Neuro-Oncology, Adnan Khan, Hamza Ibad, Kaleem Ahmed, Zahra Hoodbhoy, Muhammad Shahzad Shamim

Medical College Documents

Deep learning (DL) is a relatively newer subdomain of machine learning (ML) with incredible potential for certain applications in the medical field. Given recent advances in its use in neuro-oncology, its role in diagnosing, prognosticating, and managing the care of cancer patients has been the subject of many research studies. The gamut of studies has shown that the landscape of algorithmic methods is constantly improving with each iteration from its inception. With the increase in the availability of high-quality data, more training sets will allow for higher fidelity models. However, logistical and ethical concerns over a prospective trial comparing prognostic …

Enhancing Microbiome Host Disease Prediction With Variational Autoencoders, Celeste Manughian-Peter

Computational and Data Sciences (MS) Theses

Advancements in genetic sequencing methods for microbiomes in recent decades have permitted the collection of taxonomic and functional profiles of microbial communities, accelerating the discovery of the functional aspects of the microbiome and generating an increased interest among clinicians in applying these techniques with patients. This advancement has coincided with software and hardware improvements in the field of machine learning and deep learning. Combined, these advancements implicate further potential for progress in disease diagnosis and treatment in humans. The ability to classify a human microbiome profile into a disease category, and additionally identify the differentiating factors within the profile between …

Graphical Models In Reconstructability Analysis And Bayesian Networks, Marcus Harris, Martin Zwick

Systems Science Faculty Publications and Presentations

Reconstructability Analysis (RA) and Bayesian Networks (BN) are both probabilistic graphical modeling methodologies used in machine learning and artificial intelligence. There are RA models that are statistically equivalent to BN models and there are also models unique to RA and models unique to BN. The primary goal of this paper is to unify these two methodologies via a lattice of structures that offers an expanded set of models to represent complex systems more accurately or more simply. The conceptualization of this lattice also offers a framework for additional innovations beyond what is presented here. Specifically, this paper integrates RA and …

Diagnostic Accuracy Of Machine Learning Models To Identify Congenital Heart Disease: A Meta-Analysis, Zahra Hoodbhoy, Uswa Jiwani, Saima Sattar, Rehana A. Salam, Babar Hasan, Jai K. Das

Department of Paediatrics and Child Health

Background: With the dearth of trained care providers to diagnose congenital heart disease (CHD) and a surge in machine learning (ML) models, this review aims to estimate the diagnostic accuracy of such models for detecting CHD.
Methods: A comprehensive literature search in the PubMed, CINAHL, Wiley Cochrane Library, and Web of Science databases was performed. Studies that reported the diagnostic ability of ML for the detection of CHD compared to the reference standard were included. Risk of bias assessment was performed using Quality Assessment for Diagnostic Accuracy Studies-2 tool. The sensitivity and specificity results from the studies were used to …

Learning From Nature: From A Marine Natural Product To Synthetic Cyclooxygenase-1 Inhibitors By Automated De Novo Design., Lukas Friedrich, Gino Cingolani, Ying-Hui Ko, Mariaclara Iaselli, Morena Miciaccia, Maria Grazia Perrone, Konstantin Neukirch, Veronika Bobinger, Daniel Merk, Robert Klaus Hofstetter, Oliver Werz, Andreas Koeberle, Antonio Scilimati, Gisbert Schneider

Department of Biochemistry and Molecular Biology Faculty Papers

The repertoire of natural products offers tremendous opportunities for chemical biology and drug discovery. Natural product-inspired synthetic molecules represent an ecologically and economically sustainable alternative to the direct utilization of natural products. De novo design with machine intelligence bridges the gap between the worlds of bioactive natural products and synthetic molecules. On employing the compound Marinopyrrole A from marine Streptomyces as a design template, the algorithm constructs innovative small molecules that can be synthesized in three steps, following the computationally suggested synthesis route. Computational activity prediction reveals cyclooxygenase (COX) as a putative target of both Marinopyrrole A and the de …

Public Discussion Of Anthrax On Twitter: Using Machine Learning To Identify Relevant Topics And Events, Michele Miller, William Lee Romine, Terry L. Oroszi

Biological Sciences Faculty Publications

Background: Social media allows researchers to study opinions and reactions to events in real time. One area needing more study is anthrax-related events. A computational framework that utilizes machine learning techniques was created to collect tweets discussing anthrax, further categorize them as relevant by the month of data collection, and detect discussions on anthrax-related events. Objective: The objective of this study was to detect discussions on anthrax-related events and to determine the relevance of thetweets and topics of discussion over 12 months of data collection. Methods: This is an infoveillance study, using tweets in English containing the keyword “Anthrax” and …

Computer-Assisted Lesion Classification And Intervention Planning For Prostate Cancer, Ryan M. Alfano

Electronic Thesis and Dissertation Repository

Multi-parametric magnetic resonance imaging (mp-MRI) is emerging as a useful tool for classifying prostate cancer (PCa); however, it suffers from two major limitations: (1) complex, multi-dimensional signals make interpretation challenging and (2) inter-observer variability of lesion classification between physicians. Critically needed are methods for augmenting the interpretability of mp-MRI to assist in lesion classification. To meet this need, we leveraged a patient cohort with post-surgery pathologist-annotated transverse histology images registered to pre-surgery in-vivo mp-MRI with a measured target registration error. We developed a radiomics-based machine learning model trained on annotations for PCa vs. non-PCa, and found that a 5-feature Naïve-Bayes …

The Age Of Artificial Intelligence: Use Of Digital Technology In Clinical Nutrition, Berkeley K. Limketkai, Kasuen Mauldin, Natalie Manitius, Laleh Jalilian, Bradley R. Salonen

Faculty Research, Scholarly, and Creative Activity

Purpose of review

Computing advances over the decades have catalyzed the pervasive integration of digital technology in the medical industry, now followed by similar applications for clinical nutrition. This review discusses the implementation of such technologies for nutrition, ranging from the use of mobile apps and wearable technologies to the development of decision support tools for parenteral nutrition and use of telehealth for remote assessment of nutrition.

Recent findings

Mobile applications and wearable technologies have provided opportunities for real-time collection of granular nutrition-related data. Machine learning has allowed for more complex analyses of the increasing volume of data collected. The …

The Intersection Of Industry, Occupation, And Job Tasks With Hotel Room Cleaner Musculoskeletal Disorder Injuries: A Methods Approach To The Analysis Of California Workers’ Compensation Data, Pamela Vossenas

Dissertations and Theses

Abstract

Background

Hotel room cleaners are a high-risk occupation for musculoskeletal disorder (MSD) injuries among U.S. hotel workers. The Bureau of Labor Statistics (BLS) publishes occupational injury rate data for Maids and Housekeeping Cleaners, the Standard Occupation Classification closest to the job of a hotel room cleaner, and for the hotel industry, yet the BLS does not cross reference injury rate data by occupation and industry. This lack of occupational injury surveillance data limits MSD injury prevention and intervention efforts targeting this high-risk occupation in the hotel industry. Workers’ compensation (WC) data is another source of administrative data with potential …

Unsupervised Machine Learning For Identifying Challenging Behavior Profiles To Explore Cluster-Based Treatment Efficacy In Children With Autism Spectrum Disorder: Retrospective Data Analysis Study, Julie Gardner-Hoag, Marlena N. Novack, Chelsea Parlett-Pelleriti, Elizabeth Stevens, Dennis Dixon, Erik Linstead

Engineering Faculty Articles and Research

Background: Challenging behaviors are prevalent among individuals with autism spectrum disorder; however, research exploring the impact of challenging behaviors on treatment response is lacking.

Objective: The purpose of this study was to identify types of autism spectrum disorder based on engagement in different challenging behaviors and evaluate differences in treatment response between groups.

Methods: Retrospective data on challenging behaviors and treatment progress for 854 children with autism spectrum disorder were analyzed. Participants were clustered based on 8 observed challenging behaviors using k means, and multiple linear regression was performed to test interactions between skill mastery and treatment …

Mircorrnet: Machine Learning-Based Integration Of Mirna And Mrna Expression Profiles, Combined With Feature Grouping And Ranking., Malik Yousef, Gokhan Goy, Ramkrishna Mitra, Christine M. Eischen, Amhar Jabeer, Burcu Bakir-Gungor

Department of Cancer Biology Faculty Papers

A better understanding of disease development and progression mechanisms at the molecular level is critical both for the diagnosis of a disease and for the development of therapeutic approaches. The advancements in high throughput technologies allowed to generate mRNA and microRNA (miRNA) expression profiles; and the integrative analysis of these profiles allowed to uncover the functional effects of RNA expression in complex diseases, such as cancer. Several researches attempt to integrate miRNA and mRNA expression profiles using statistical methods such as Pearson correlation, and then combine it with enrichment analysis. In this study, we developed a novel tool called miRcorrNet, …

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 …

Imaging Based Prediction Of Pathology In Adult Diffuse Glioma With Applications To Therapy And Prognosis, Evan Gates

Dissertations & Theses (Open Access)

The overall aggressiveness of a glioma is measured by histologic and molecular analysis of tissue samples. However, the well-known spatial heterogeneity in gliomas limits the ability for clinicians to use that information to make spatially specific treatment decisions. Magnetic resonance imaging (MRI) visualizes and assesses the tumor. But, the exact degree to which MRI correlates with the actual underlying tissue characteristics is not known.

In this work, we derive quantitative relationships between imaging and underlying pathology. These relations increase the value of MRI by allowing it to be a better surrogate for underlying pathology and they allow evaluation of the …

The Future Of Artificial Intelligence In The Healthcare Industry, Erika Bonnist

Honors Theses

Technology has played an immense role in the evolution of healthcare delivery for the United States and on an international scale. Today, perhaps no innovation offers more potential than artificial intelligence. Utilizing machine intelligence as opposed to human intelligence for the purposes of planning, offering solutions, and providing insights, AI has the ability to alter traditional dynamics between doctors, patients, and administrators; this reality is now producing both elation at artificial intelligence's medical promise and uncertainty regarding its capacity in current systems. Nevertheless, current trends reveal that interest in AI among healthcare stakeholders is continuously increasing, and with the current …

Prediction Of Throwing Distance In The Men’S And Women’S Javelin Final At The 2017 Iaaf World Championships, John Krzyszkowski, Kristof Kipp

Exercise Science Faculty Research and Publications

The purpose of this study was to use regularised regression models to identify the most important biomechanical predictors of throwing distance in elite male (M) and female (F) javelin throwers at the 2017 IAAF world championships. Biomechanical data from 13 male and 12 female javelin throwers who competed at the 2017 IAAF world championships were obtained from an official scientific IAAF report. Regularised regression models were used to investigate the associations between throwing distance and release parameters, whole-body kinematic and joint-level kinematic data. The regularised regression models identified two biomechanical predictors of throwing distances in both M and F javelin …

Investigating Diffusion Tensor Imaging Correlates Of Cognitive Impairment In Idiopathic Normal Pressure Hydrocephalus And Alzheimer's Disease, Omar Hasan, Omar Hasan

Dissertations & Theses (Open Access)

Modest expansion of the human brain cerebrospinal fluid (CSF)-filled ventricles is normal with aging, and because of this, it can be difficult for physicians to accurately diagnose and treat enlarged ventricles (ventriculomegaly), called hydrocephalus¹ (fluid or water in the brain) Ventriculomegaly occurs due to an obstruction (such as a blood clot or tumor), or a change in CSF absorption². Primary hydrocephalus, also called idiopathic normal pressure hydrocephalus (iNPH), is non-obstructive and may be comorbid with other neurodegenerative diseases such as Alzheimer’s disease (AD) or frontotemporal dementia (FTD). Clinically, it can be difficult to tell whether the pathophysiological …