Applying Blockchain Technology to Enhance Clinical Trial Recruitment
Patient recruitment for clinical trials is known to be a challenging aspect of clinical research. There are multiple competing concerns from the sponsor, patient and principal investigator’s perspectives resulting in most clinical trials not meeting recruitment requirements on time. Conducting under-enrolled clinical trials affects the power of conclusive results or causes premature trial termination. Blockchain is a distributed ledger technology originally applied in the financial sector. Its features as a peer-to-peer system with publicly audited transactions, data security, and patient privacy are a good fit for the needs of clinical trials recruitment. The “Smart Contract” is a programmable self-executing protocol…
Graduate Research Assistantship in Biomedical Imaging Informatics (available Aug. 2019):
Many heart patients have not benefitted from MR scans due to technical limitations. A Ph.D. student is sought to further develop the new TRENDimaging software package to analyze complex and irregular heart motions. This will support wider application of ultrafast cardiac MR scans. The position is supported by a new grant from the American Heart Association. More information about the research group is available at: https://cafnrfaculty.missouri.edu/vandoren/ Those with background or interests in scientific computing, biomedical imaging, software engineering, and / or statistical approaches of data science are invited to submit a resume with contact information for two to three…
Dr. Eileen Avery takes the helm as the new Executive Director of the University of Missouri Research Data Center (MU RDC) and Population, Education, and Health Center (PEHC).
Dr. J. Chris Pires led a multi-institutional team to study vegetable family tree for better food and published in Nature Communications
Dr. Chris Pires led a multi-institutional team to study vegetable family tree for better food and published in Nature Communications. https://www.sciencedaily.com/releases/2019/07/190708154106.htm…
Discovery of Homogeneous Subgroups from Heterogeneous Populations for Precision Health – A Deep Exploratory Mining and X2AI Approach
Today, six of the top ten highest-grossing drugs in the US are effective in less than 10% of patients and even the most effective drugs from that list have positive responses in only 25% of patients. This “imprecision medicine” practice not only harms certain populations of patients, it also burdens the healthcare system financially. By finding meaningful and homogeneous subgroups prior to conducting costly clinical trials, researchers can further study focused populations and identify potential risk factors through slicing and dicing from complex phenotypic/genotypic information sources. Advancements in machine learning algorithms have shown promising results in many biomedical applications and…
Using Predictive Analytics to Improve Surveillance of Heat-Related Illnesses During Military Training
Heat-related illnesses are important occupational risks in military personnel, especially for soldiers who do not have experience with hot climate regions. Our study focuses on predictive analysis of heat-related illnesses to improve prevention program. The Royal Thai Army (RTA) Medical Department collected data from conscripts during a 10-week military training program in 2013. To build predictive analytic models, we applied various machine learning and deep learning methods, including generalized linear model (GLM), k-nearest neighbors (kNN), random forests (RF), eXtreme gradient boosting (XGB), deep neural networks (DNN), and convolutional neural networks (CNN). We compared accuracy between different models and we found…
Uncovering complex relationships in biomedical data through hierarchical logic pattern contrast mining
Understanding the relationships which exist between features in medical data ranging from electronic health records to genetic variations in sequenced genomes is key to understanding how these features impact the medical condition of an individual. Existing pattern mining methods are unable to discover relationships more complex than co-occurrence which limits their usefulness in searching for patterns associated with medical conditions which may include inhibitory and many-to-one relationships between features. Our algorithm can extract complex nested logical relationships which can provide additional information about how individual features interact to affect medical outcomes. We demonstrate the effectiveness of our algorithm on two…
Linking EMR and Exposome Data for Risk Prediction and Interventions: A Translational Approach
Precision medicine (PM) is a medical model that proposes the customization of healthcare, with medical decisions, treatments, practices, or products being tailored to the individual patient. An individual’s “Social Determinants of Health” (SDOH) have been demonstrated as a key factor in obtaining successful clinical outcomes for individual patients which necessitate individualized interventions. Two major problems exist in addressing SDOH within a clinical setting. First, interventions that have shown to be successful in addressing challenges presented by various Social Determinants of Health often scarce and span outside of those services that are available and/or reimbursed within a healthcare setting. Because of this, it is critical…
Image Segmentation in Colorectal Tissue Slides Using Denoising Autoencoder
Colorectal cancer (CRC) is a common tumor type with variable treatment course. Given the high availability of histological slides and wealth of the prognostic information the slide images may provide, it is important to conduct corresponding image analysis in high-throughput fashion. In this presentation, we will discuss a segmentation approach based on denoising autoencoder for colorectal whole slide images using annotated image patches.