2019 — U-HACK MED

U-Hack Med 2019 Outcomes

Lyda Hill Department of BioinformaticsNovember 15, 2019

TEAM 1: Hippocampal oscillatory patterns during associate recognition memory

Investigate hippocampal oscillatory patterns to test predictions of dual process theory to classify recognition versus recollection using memory retrieval data.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centermachine learning, Team 1, hackathon, u-hack med, UT Southwestern Medical Center, Lyda Hill Department of Bioinformatics, Bo Li, memory retrieval, classifier, bradley lega, srinivas kota, neurosurgery

team 2: A Deep convolutional neural network for identifying thyroid cancer

Develop a more accurate system for identifying thyroid cancer by applying a deep convolutional neural network algorithm to data from a UTSW thyroid cancer database.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerhackathon, u-hack med, UT Southwestern Medical Center, Lyda Hill Department of Bioinformatics, fiemu nwariaku, surgery, dcnn, deep learning, neural network, machine learning, thyroid cancer, thyroid nodule

Team 3: MONITORING POST-OPERATIVE HEAD-NECK CANCER PATIENTS VIA SELF-POWERED SMART INSOLES

Develop smart insoles to measure post-operative patient data and transmit via smart phone app to cloud storage for physician office for data analysis.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019Lyda Hill Department of Bioinformatics, u-hack med, hackathon, UT Southwestern Medical Center, steve jiang, guanglin tang, maia lab, robotics, smart wearables, head neck cancer

Team 4: DEFINITION OF CELL CYCLE STATE IN LABEL FREE IMAGES

Test an approach to development of a label-free alternative to the FUCCI probe for detection of cell cycle state using a convolutional neuronal network that classifies label-free images as corresponding to a particular cell cycle phase.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerhackathon, u-hack med, UT Southwestern Medical Center, Lyda Hill Department of Bioinformatics, fucci, fluorescent ubiquitination-based cell cycle indicator, mitosis, non-fluorescence microscopy, murat cobanoglu, tadamoto isogai

Team 5: Stratification of SLE patient cohort for precision medicine

Create analytical tools to stratify lupus patients into subsets based on significant differences in genetics and clinical characteristics.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical CenterLyda Hill Department of Bioinformatics, UT Southwestern Medical Center, u-hack med, hackathon, prithvi raj, lupus, sle, precision medicine, classification, subsetting, stratification, immunology

Team 6: The isoform potential of the genome

Maximize utility of gene ontology analysis thru creation of a genome browser track making protein isoform prediction accessible to the broader biomedical research community.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centeru-hack med, UT Southwestern Medical Center, Lyda Hill Department of Bioinformatics, hackathon, dustin hancks, immunology, genomics, splicing, protein isoforms

Team 7: Using Machine Learning to Improve Post-Acute Rehabilitation Process Flow

Develop a prediction model to streamline the transition of care process for hospital patients transitioning into rehabilitation facilities, with the goal of decreasing acute length of stay, facilitating outside referrals when needed and decreasing attrition from our health system.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerdeep learning, tumor classification, annotation, image segmentation, hackathon, u-hack med, UT Southwestern Medical Center, Lyda Hill Department of Bioinformatics, bioinformatics, Satwik Rajaram

Team 8: Automated algorithm for detecting and localizing enteric tubes to facilitate accurate and timely radiographic interpretation

Create an image analysis tool to accurately localize enteric tubes and flag potential problems on radiographs in clinical practice.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerdeep learning, pathology, tumor classification, annotation, image segmentation, u-hack med, UT Southwestern Medical Center, hackathon, Lyda Hill Department of Bioinformatics, bioinformatics, gaudenz danuser, andrew jamieson

Team 9: Real-Time Cardiac Assessment of Catheterization-Derived Fick and CMR-Derived Flow

Improve workflow, reliability and reproduciblity of interventional cardiac MRI through a web/app-based user-friendly display for real-time assessment.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centerimage segmentation, machine learning, cardiology, neuroscience, MRI, u-hack med, UT Southwestern Medical Center, hackathon, Lyda Hill Department of Bioinformatics, bioinformatics, albert montillo

Team 10: novel clinical prediction approaches to managing care for acute Pulmonary Embolism patients

Derive an optimally accurate prediction rule for 48-hour severe adverse events in acute pulmonary embolism patients.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019predictive analytics, visualization, pediatric, endocrinology, Soumaya Adhikari, Lyda Hill Department of Bioinformatics, hackathon, UT Southwestern Medical Center, u-hack med

Team 11: Optical coherence tomography-angiography in glaucoma

Develop a deep learning tool that will parse OCTA data facilitating diagnosis of early glaucoma and its progression and that will rule out non-glaucoma conditions.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centernatural language processing, clinical trials, u-hack med, UT Southwestern Medical Center, hackathon, Lyda Hill Department of Bioinformatics, bioinformatics, jeffrey gagan, brandi cantarel, ngs, clia

Team 12: Rapid evaluation of statistical strategies for predicting interactions and information transfer between proteins

Rapidly organize, statistically describe, and filter data and evaluate the performance of alternative models for phylogenetic correction on a gold-standard test set of protein interaction data.

Project DescriptionsLyda Hill Department of BioinformaticsMay 30, 2019UT Southwestern Medical Centermachine learning, liver disease, clinical staging, lipidomics, UT Southwestern Medical Center, u-hack med, Lyda Hill Department of Bioinformatics, hackathon, molecular genetics, jeffrey mcdonald

2019 TEAMS