Physical Sciences and Mathematics Commons^™

A New Efficient Method To Detect Genetic Interactions For Lung Cancer Gwas, Jennifer Luyapan, Xuemei Ji, Siting Li, Xiangjun Xiao, Dakai Zhu, Eric J. Duell, David C. Christiani, Matthew B. Schabath, Susanne M. Arnold, Shanbeh Zienolddiny, Hans Brunnström, Olle Melander, Mark D. Thornquist, Todd A. Mackenzie, Christopher I. Amos, Jiang Gui

Markey Cancer Center Faculty Publications

BACKGROUND: Genome-wide association studies (GWAS) have proven successful in predicting genetic risk of disease using single-locus models; however, identifying single nucleotide polymorphism (SNP) interactions at the genome-wide scale is limited due to computational and statistical challenges. We addressed the computational burden encountered when detecting SNP interactions for survival analysis, such as age of disease-onset. To confront this problem, we developed a novel algorithm, called the Efficient Survival Multifactor Dimensionality Reduction (ES-MDR) method, which used Martingale Residuals as the outcome parameter to estimate survival outcomes, and implemented the Quantitative Multifactor Dimensionality Reduction method to identify significant interactions associated with age of …

Integrated Multiparametric Radiomics And Informatics System For Characterizing Breast Tumor Characteristics With The Oncotypedx Gene Assay, Michael A. Jacobs, Christopher B. Umbricht, Vishwa S. Parekh, Riham H. El Khouli, Leslie Cope, Katarzyna J. Macura, Susan Harvey, Antonio C. Wolff

Radiology Faculty Publications

Optimal use of multiparametric magnetic resonance imaging (mpMRI) can identify key MRI parameters and provide unique tissue signatures defining phenotypes of breast cancer. We have developed and implemented a new machine-learning informatic system, termed Informatics Radiomics Integration System (IRIS) that integrates clinical variables, derived from imaging and electronic medical health records (EHR) with multiparametric radiomics (mpRad) for identifying potential risk of local or systemic recurrence in breast cancer patients. We tested the model in patients (n = 80) who had Estrogen Receptor positive disease and underwent OncotypeDX gene testing, radiomic analysis, and breast mpMRI. The IRIS method was trained …

Exploring The Eating Disorder Examination Questionnaire, Clinical Impairment Assessment, And Autism Quotient To Identify Eating Disorder Vulnerability: A Cluster Analysis, Natalia Stewart Rosenfield, Erik Linstead

Engineering Faculty Articles and Research

Eating disorders are very complicated and many factors play a role in their manifestation. Furthermore, due to the variability in diagnosis and symptoms, treatment for an eating disorder is unique to the individual. As a result, there are numerous assessment tools available, which range from brief survey questionnaires to in-depth interviews conducted by a professional. One of the many benefits to using machine learning is that it offers new insight into datasets that researchers may not previously have, particularly when compared to traditional statistical methods. The aim of this paper was to employ k-means clustering to explore the Eating Disorder …

Comparison Of Machine Learning Models: Gesture Recognition Using A Multimodal Wrist Orthosis For Tetraplegics, Charlie Martin

The Journal of Purdue Undergraduate Research

Many tetraplegics must wear wrist braces to support paralyzed wrists and hands. However, current wrist orthoses have limited functionality to assist a person’s ability to perform typical activities of daily living other than a small pocket to hold utensils. To enhance the functionality of wrist orthoses, gesture recognition technology can be applied to control mechatronic tools attached to a novel fabricated wrist brace. Gesture recognition is a growing technology for providing touchless human-computer interaction that can be particularly useful for tetraplegics with limited upper-extremity mobility. In this study, three gesture recognition models were compared—two dynamic time-warping models and a hidden …

Emerging Technologies In Healthcare: Analysis Of Unos Data Through Machine Learning, Reyhan Merekar

Student Theses and Dissertations

The healthcare industry is primed for a massive transformation in the coming decades due to emerging technologies such as Artificial Intelligence (AI) and Machine Learning. With a practical application to the UNOS (United Network of Organ Sharing) database, this Thesis seeks to investigate how Machine Learning and analytic methods may be used to predict one-year heart transplantation outcomes. This study also sought to improve on predictive performances from prior studies by analyzing both Donor and Recipient data. Models built with algorithms such as Stacking and Tree Boosting gave the highest performance, with AUC’s of 0.6810 and 0.6804, respectively. In this …

Ml-Medic: A Preliminary Study Of An Interactive Visual Analysis Tool Facilitating Clinical Applications Of Machine Learning For Precision Medicine, Laura Stevens, David Kao, Jennifer Hall, Carsten Görg, Kaitlyn Abdo, Erik Linstead

Engineering Faculty Articles and Research

Accessible interactive tools that integrate machine learning methods with clinical research and reduce the programming experience required are needed to move science forward. Here, we present Machine Learning for Medical Exploration and Data-Inspired Care (ML-MEDIC), a point-and-click, interactive tool with a visual interface for facilitating machine learning and statistical analyses in clinical research. We deployed ML-MEDIC in the American Heart Association (AHA) Precision Medicine Platform to provide secure internet access and facilitate collaboration. ML-MEDIC’s efficacy for facilitating the adoption of machine learning was evaluated through two case studies in collaboration with clinical domain experts. A domain expert review was also …

Development Of Fully Balanced Ssfp And Computer Vision Applications For Mri-Assisted Radiosurgery (Mars), Jeremiah Sanders

Dissertations & Theses (Open Access)

Prostate cancer is the second most common cancer in men and the second-leading cause of cancer death in men. Brachytherapy is a highly effective treatment option for prostate cancer, and is the most cost-effective initial treatment among all other therapeutic options for low to intermediate risk patients of prostate cancer. In low-dose-rate (LDR) brachytherapy, verifying the location of the radioactive seeds within the prostate and in relation to critical normal structures after seed implantation is essential to ensuring positive treatment outcomes.

One current gap in knowledge is how to simultaneously image the prostate, surrounding anatomy, and radioactive seeds within the …

Artificial Intelligence: A New Paradigm In Obstetrics And Gynecology Research And Clinical Practice, Pulwasha Iftikhar, Marcela V. Kuijpers, Azadeh Khayyat, Aqsa Iftikhar, Maribel Degouvia De Sa

Publications and Research

Artificial intelligence (AI) is growing exponentially in various fields, including medicine. This paper reviews the pertinent aspects of AI in obstetrics and gynecology (OB/GYN) and how these can be applied to improve patient outcomes and reduce the healthcare costs and workload for clinicians.

Herein, we will address current AI uses in OB/GYN, and the use of AI as a tool to interpret fetal heart rate (FHR) and cardiotocography (CTG) to aid in the detection of preterm labor, pregnancy complications, and review discrepancies in its interpretation between clinicians to reduce maternal and infant morbidity and mortality. AI systems can be used …

Machine Learning Prediction Of Glioblastoma Patient One-Year Survival, Andrew Du '20, Warren Mcgee, Jane Y. Wu

Student Publications & Research

Glioblastoma (GBM) is a grade IV astrocytoma formed primarily from cancerous astrocytes and sustained by intense angiogenesis. GBM often causes non-specific symptoms, creating difficulty for diagnosis. This study aimed to utilize machine learning techniques to provide an accurate one-year survival prognosis for GBM patients using clinical and genomic data from the Chinese Glioma Genome Atlas. Logistic regression (LR), support vector machines (SVM), random forest (RF), and ensemble models were used to identify and select predictors for GBM survival and to classify patients into those with an overall survival (OS) of less than one year and one year or greater. With …

Model-Based Machine Learning To Identify Clinical Relevance In A High-Resolution Simulation Of Sepsis And Trauma, Zachary H. Silberman Md, Robert Chase Cockrell Phd, Gary An Md

Larner College of Medicine Fourth Year Advanced Integration Teaching/Scholarly Projects

Introduction: Sepsis is a devastating, costly, and complicated disease. It represents the summation of varied host immune responses in a clinical and physiological diagnosis. Despite extensive research, there is no current mediator-directed therapy, nor a biomarker panel able to categorize disease severity or reliably predict outcome. Although still distant from direct clinical translation, dynamic computational and mathematical models of acute systemic inflammation and sepsis are being developed. Although computationally intensive to run and calibrate, agent-based models (ABMs) are one type of model well suited for this. New analytical methods to efficiently extract knowledge from ABMs are needed. Specifically, machine-learning …

Improving The Accessibility And Transferability Of Machine Learning Algorithms For Identification Of Animals In Camera Trap Images: Mlwic2, Michael A. Tabak, Mohammad S. Norouzzadeh, David W. Wolfson, Erica J. Newton, Raoul K. Boughton, Jacob S. Ivan, Eric Odell, Eric S. Newkirk, Reesa Y. Conrey, Jennifer Stenglein, Fabiola Iannarilli, John Erb, Ryan K. Brook, Amy J. Davis, Jesse Lewis, Daniel P. Walsh, James C. Beasley, Kurt C. Vercauteren, Jeff Clune, Ryan S. Miller

USDA Wildlife Services: Staff Publications

Motion-activated wildlife cameras (or “camera traps”) are frequently used to remotely and noninvasively observe animals. The vast number of images collected from camera trap projects has prompted some biologists to employ machine learning algorithms to automatically recognize species in these images, or at least filter-out images that do not contain animals. These approaches are often limited by model transferability, as a model trained to recognize species from one location might not work as well for the same species in different locations. Furthermore, these methods often require advanced computational skills, making them inaccessible to many biologists. We used 3 million camera …

Singlet Oxygen Generation By Porphyrins And Metalloporphyrins Revisited: A Quantitative Structure-Property Relationship (Qspr) Study, Andrey A. Buglak, Mikhail Filatov, Althaf M. Hussain, Manabu Sugimoto

Books/Book chapters

state followed by formation of singlet oxygen (1O2), which is a highly reactive species and mediates various oxidative processes. The design of advanced sensitizers based on porphyrin compounds have attracted significant attention in recent years. However, it is still difficult to predict the efficiency of singlet oxygen generation for a given structure. Our goal was to develop a quantitative structure-property relationship (QSPR) model for the fast virtual screening and prediction of singlet oxygen quantum yields for pophyrins and metalloporphyrins. We performed QSPR analysis of a dataset containing 32 compounds, including various porphyrins and their analogues (chlorins and bacteriochlorins). Quantum-chemical descriptors …

Modulation Of Medical Condition Likelihood By Patient History Similarity, Jonathan Turner, Dympna O'Sullivan, Jon Bird

Articles

Introduction: We describe an analysis that modulates the simple population prevalence derived likelihood of a particular condition occurring in an individual by matching the individual with other individuals with similar clinical histories and determining the prevalence of the condition within the matched group.

Methods: We have taken clinical event codes and dates from anonymised longitudinal primary care records for 25,979 patients with 749,053 recorded clinical events. Using a nearest neighbour approach, for each patient, the likelihood of a condition occurring was adjusted from the population prevalence to the prevalence of the condition within those patients with the closest …