Diagnosis Commons^™

Moral Injury To Inform Analysis Of Post-Traumatic Stress Disorder, Amanda Julia Manea

Senior Theses

Post-traumatic stress disorder (PTSD) is a mental health condition that almost one out of ten veterans struggle with. Although the National Center for PTSD has made extensive progress in characterizing and developing new treatments for PTSD, most veterans still experience symptoms of PTSD following treatment. Novel avenues of investigation, such as developing algorithms to review electronic health record (EHR) data and better understanding moral injury, are being pursued to address the gap that still exists when it comes to treating veterans. Moral injury is the individual evaluation of exposure to a potentially morally injurious event (PMIE) and can lead to …

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 …

Computer Aided Diagnosis System For Breast Cancer Using Deep Learning., Asma Baccouche

Electronic Theses and Dissertations

The recent rise of big data technology surrounding the electronic systems and developed toolkits gave birth to new promises for Artificial Intelligence (AI). With the continuous use of data-centric systems and machines in our lives, such as social media, surveys, emails, reports, etc., there is no doubt that data has gained the center of attention by scientists and motivated them to provide more decision-making and operational support systems across multiple domains. With the recent breakthroughs in artificial intelligence, the use of machine learning and deep learning models have achieved remarkable advances in computer vision, ecommerce, cybersecurity, and healthcare. Particularly, numerous …

Succinic Semialdehyde Dehydrogenase Deficiency (Ssadhd): Qualitative Needs Assessment For Patients With A Rare Neurological Disorder, Cassandra Bovee, Kayla Woodring

Annual Research Symposium

No abstract provided.

C37: Evaluation Of The Diagnostic Efficacy Of Saliva For Detection Of Dengue Antibodies- An Elisa Study, Vidya Gurram Shankar

Annual Research Symposium

No abstract provided.

Predicting Postoperative Delirium Risk For Intracranial Surgery: A Statistical Machine Learning Approach, Juliet Aygun, Alaina Bartfeld, Sahana Rayan

The Journal of Purdue Undergraduate Research

No abstract provided.

Development Of Gaussian Learning Algorithms For Early Detection Of Alzheimer's Disease, Chen Fang

FIU Electronic Theses and Dissertations

Alzheimer’s disease (AD) is the most common form of dementia affecting 10% of the population over the age of 65 and the growing costs in managing AD are estimated to be $259 billion, according to data reported in the 2017 by the Alzheimer's Association. Moreover, with cognitive decline, daily life of the affected persons and their families are severely impacted. Taking advantage of the diagnosis of AD and its prodromal stage of mild cognitive impairment (MCI), an early treatment may help patients preserve the quality of life and slow the progression of the disease, even though the underlying disease cannot …

Large-Scale Genome-Wide Meta-Analysis Of Polycystic Ovary Syndrome Suggests Shared Genetic Architecture For Different Diagnosis Criteria, Felix Day, Tugce Karaderi, Michelle R. Jones, Cindy Meun, Chunyan He, Alex Drong, Peter Kraft, Nan Lin, Hongyan Huang, Linda Broer, Reedik Magi, Richa Saxena, Triin Laisk, Margrit Urbanek, M. Geoffrey Hayes, Gudmar Thorleifsson, Juan Fernandez-Tajes, Anubha Mahajan, Benjamin H. Mullin, Bronwyn G. A. Stuckey, Timothy D. Spector, Scott G. Wilson, Mark O. Goodarzi, Lea Davis, Barbara Obermayer-Pietsch, André G. Uitterlinden, Verneri Anttila, Benjamin M. Neale, Marjo-Riitta Jarvelin, Bart Fauser

Internal Medicine Faculty Publications

Polycystic ovary syndrome (PCOS) is a disorder characterized by hyperandrogenism, ovulatory dysfunction and polycystic ovarian morphology. Affected women frequently have metabolic disturbances including insulin resistance and dysregulation of glucose homeostasis. PCOS is diagnosed with two different sets of diagnostic criteria, resulting in a phenotypic spectrum of PCOS cases. The genetic similarities between cases diagnosed based on the two criteria have been largely unknown. Previous studies in Chinese and European subjects have identified 16 loci associated with risk of PCOS. We report a fixed-effect, inverse-weighted-variance meta-analysis from 10,074 PCOS cases and 103,164 controls of European ancestry and characterisation of PCOS related …

Bayesian Analytical Approaches For Metabolomics : A Novel Method For Molecular Structure-Informed Metabolite Interaction Modeling, A Novel Diagnostic Model For Differentiating Myocardial Infarction Type, And Approaches For Compound Identification Given Mass Spectrometry Data., Patrick J. Trainor

Electronic Theses and Dissertations

Metabolomics, the study of small molecules in biological systems, has enjoyed great success in enabling researchers to examine disease-associated metabolic dysregulation and has been utilized for the discovery biomarkers of disease and phenotypic states. In spite of recent technological advances in the analytical platforms utilized in metabolomics and the proliferation of tools for the analysis of metabolomics data, significant challenges in metabolomics data analyses remain. In this dissertation, we present three of these challenges and Bayesian methodological solutions for each. In the first part we develop a new methodology to serve a basis for making higher order inferences in metabolomics, …

The Acquisition And Analysis Of Electroencephalogram Data For The Classification Of Benign Partial Epilepsy Of Childhood With Centrotemporal Spikes, Jessica A. Scarborough

Master's Theses

In this thesis, I will expand upon each step in the process of acquiring and analyzing electroencephalogram (EEG) for the classification of benign childhood epilepsy with centrotemporal spikes. Despite huge advancements in the field of health informatics—natural language processing, machine learning, predictive modeling—there are significant barriers to the access of clinical data. These barriers include information blocking, privacy policy concerns, and a lack of stakeholder support. We will see that these roadblocks are all responsible for stunting biomedical research in some way, including my own experiences in acquiring the data for the second chapter of this thesis.

This second chapter …

Longitudinal Measurement And Hierarchical Classification Framework For The Prediction Of Alzheimer's Disease, Meiyan Huang, Wei Yang, Qianjin Feng, Wufan Chen, Michael Weiner, Paul Aisen, Ronald Petersen, Clifford R. Jack Jr., William Jagust, John Trojanowki, Arthur W. Toga, Laurel Beckett, Robert C. Green, Andrew Saykin, John Morris, Leslie M. Shaw, Jeffrey Kaye, Joseph Quinn, Lisa Silbert, Betty Lind, Raina Carter, Sara Dolen, Lon S. Schneider, Sonia Pawluczyk, Mauricio Beccera, Liberty Teodoro, Bryan Spann, James Brewer, Helen Vanderswag, Adam Fleisher, Charles D. Smith, Greg A. Jicha, Peter A. Hardy, Partha Sinha, Elizabeth Oates, Gary Conrad

Neurology Faculty Publications

Accurate prediction of Alzheimer’s disease (AD) is important for the early diagnosis and treatment of this condition. Mild cognitive impairment (MCI) is an early stage of AD. Therefore, patients with MCI who are at high risk of fully developing AD should be identified to accurately predict AD. However, the relationship between brain images and AD is difficult to construct because of the complex characteristics of neuroimaging data. To address this problem, we present a longitudinal measurement of MCI brain images and a hierarchical classification method for AD prediction. Longitudinal images obtained from individuals with MCI were investigated to acquire important …

Development Of Anatomical And Functional Magnetic Resonance Imaging Measures Of Alzheimer Disease, Samaneh Kazemifar

Electronic Thesis and Dissertation Repository

Alzheimer disease is considered to be a progressive neurodegenerative condition, clinically characterized by cognitive dysfunction and memory impairments. Incorporating imaging biomarkers in the early diagnosis and monitoring of disease progression is increasingly important in the evaluation of novel treatments. The purpose of the work in this thesis was to develop and evaluate novel structural and functional biomarkers of disease to improve Alzheimer disease diagnosis and treatment monitoring. Our overarching hypothesis is that magnetic resonance imaging methods that sensitively measure brain structure and functional impairment have the potential to identify people with Alzheimer’s disease prior to the onset of cognitive decline. …

Image Quality Of Energy-Dependent Approaches For X-Ray Angiography, Jesse Evan Tanguay

Electronic Thesis and Dissertation Repository

Digital subtraction angiography (DSA) is an x-ray-based imaging method widely used for diagnosis and treatment of patients with vascular disease. This technique uses subtraction of images acquired before and after injection of an iodinated contrast agent to generate iodine-specific images. While it is extremely successful at imaging structures that are near-stationary over a period of several seconds, motion artifacts can result in poor image quality with uncooperative patients and DSA is rarely used for coronary applications.

Alternative methods of generating iodine-specific images with reduced motion artifacts might exploit the energy-dependence of x-ray attenuation in a patient. This could be performed …