Sep. 24, 2019
Addressing Challenges of Deep Learning for Post-Disaster Damage Classification
The effectiveness of natural disaster response functions has increasingly relied on information extracted from remotely-sensed imagery – particularly for the time-critical and often large-scale task of assessing post-disaster damage to the built environment. Despite a recent push to operationalize deep learning (DL) technologies to assist this task, several overlooked and unaddressed challenges limit DL’s applicability. First, very little DL research in the remote sensing domain utilizes the geographic variation in objects of interest or ancillary spatial data layers as features which could help the algorithms learn. Second, there is a scarcity of labelled training data depicting the effects of common…
Sep. 11, 2019
The Role of Informatics in the Implementation of Population Precision Health
Abstract: Precision health is an emerging concept in healthcare. Coupled with the movement towards Learning Healthcare Systems, there is an opportunity to transform how care and prevention are imagined. To realize this vision requires collection and analysis of large amounts of disparate data, development of knowledge from the data, and seamless ways to deliver the knowledge back to clinicians and, increasingly, patients. Informatics is expected to play a key role in precision health. In this presentation, Dr. Williams will provide some background and philosophy on different terms in current use, will introduce Geisinger’s MyCode Community Health Initiative—the largest implemented population…
Aug. 28, 2019
Using Geospatial Context: Facilitating Geospatial Analysis in Research
This presentation will briefly review the Geospatial Context Big Table status and its current evolution. Then the presentation turns to two projects that have used or are using geospatial context in the analysis and visualization of their results. The first project is a zoonotic disease study originally undertaken as a class project and lab rotation. The second is a study of Thyroid Cancer and possible exposome radiation influences and population stability metrics. In each case focus will be on developing focus and assembling data and possible surrogates, compiling into units of analysis, and the modeling of those relationships.
Aug. 19, 2019
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…
July 25, 2019
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…
July 10, 2019
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).
July 10, 2019
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…
May 6, 2019
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…
May 1, 2019
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…