Three NSF RAPID grants led by MUIDSI Core Faculty to Tackle COVID-19 Pandemic
MUIDSI core faculty are leading the following ongoing NSF RAPID projects for COVID-19: Dr. Steven Van Doren is the sole PI for “RAPID: Structure of Membrane-Bound Fusion Peptide of SARS-CoV-2 Required for Infection.” Dr. Praveen Rao is the PI for “RAPID: Democratizing Genome Sequence Analysis for COVID-19 Using CloudLab” with Co-PIs Drs. Wesley Warren, Peter Tonellato, Eduardo J. Simoes, and Deepthi Rao. Dr. Chi-Ren Shyu is the PI for “RAPID: Geospatially-Enabled Deep Analytics for Real-time Mitigation and Response to COVID-19 Outbreak for American Rural Populations” with Co-PIs Drs. Mirna Becevic,…
Artificial Intelligence Driven Framework for the Structurization of Free-Text Diagnostic Reports
Diagnosticians record, share, and store a wealth of data on patients, diseases, and biomedical processes in free-text diagnostic reports. To continue providing advanced biomedical services, healthcare organizations should efficiently and effectively perform complex data management, aggregate data resources, and ensure the interconnectivity and interoperability of biomedical data sets. However, free-text is a poor starting point for the computational analysis of complex biomedical information. For data management applications, diagnostic reports, biomedical test results, diagnostic images must be in a structured and machine-readable format. Free-text diagnostic reports lack data structure, making it challenging to extract information and use it for medical care…
Cancer Research Funding: Where is the Money?
Cancer is one of the most common and deadly diseases and its incidence is increasing. There are over one hundred types of cancer and they have a varied impact on society and those affected. Some have known, preventable causes and some are poorly understood. Some can be detected early and some are only detected in an advanced stage. Some are very treatable and some have a very high mortality rate. In order to level the playing field for cancers, there needs to be research to understand more about poorly understood cancers. What is the present state of cancer research funding?…
Quantifying the Predictive Value of Categories of Neighborhood-Level Risk Factors to Predict Health Outcomes
A person’s environmental context is well known to impact health outcomes. However, this information is rarely available in an acute clinical care setting. Although a growing body of literature combines the information available in the Electronic Medical Record (EMR) with environmental and place-based data to better examine the environmental impact on health, these studies generally focus on a single index or category of environmental data, thus failing to take into account the richness of geo-spatial data that are available, as well as the underlying interactions of multiple community risk factors. Recent improvements in access to geo-spatial data at multiple layers…
A Case-Control based Genomic Analysis of Chronic Obstructive Pulmonary Disease
Chronic Obstructive Pulmonary Disease (COPD) is a respiratory illness that affects millions of people all over the world. It is a major cause of chronic morbidity and mortality and a serious global public health problem. COPD is the fourth leading cause of death worldwide. Although the environmental causes of COPD which predominantly include cigarette smoking are well-documented, to this date the genetic underpinnings of COPD remain largely unknown. Furthermore in the current landscape of a respiratory pandemic, COPD patients are at a much higher risk for developing other respiratory illnesses and co-morbidities. In this study we use genomic data from…
Proof tree contrast mining for automatic hypothesis generation
The probability of developing almost any given disease is affected by multiple risk factors. These risk factors often do not behave independently, instead interacting in specific ways which affect the probability of developing the disease. To better understand the root causes of many diseases, it is necessary to study these interactions as they may provide clues about the underlying mechanisms responsible for the development of the disease. We have developed a method for studying these interactions by applying contrast mining to extract patterns of nested logical interactions associated with specific medical outcomes. We demonstrate the effectiveness of this method in…
Bioinformatic Prediction of the Potential SARS-CoV-2 Receptors in Human
Unraveling receptors used by SARS-CoV-2 for entry and the exact positively selected sites on the Spike (S) protein associated with this process can provide insights into the viral transmission and reveal therapeutic targets. Except for ACE2, accumulating evidence indicates that the S protein potentially recognizes other receptors like CD147. Therefore, methods for new receptors identification are urgently needed. To account for this, the following three aspects will be explored in this proposal. First, with increasing genome sequence data to investigate evolution and selection patterns and to assess their influence on the structure and function of the S protein. Second, integrating…
New Algorithmic Strategies to Forecasting Contagions
Abstract Can tweets be really used to track and forecast an epidemic outbreak? Can we monitor a handful of individuals in a city or a social network to forecast virality? The problems we focus on occur in diverse areas: social networks, public health surveillance, and cybersecurity. This talk will comprise two parts. In the first part, we leverage graphical models to infer the progression of the flu from user tweets. We show how we can effectively and accurately track flu trends and peaks using millions of tweets harvested from Latin America. In the second part, we design social network sensors for early detection…
CHALLENGES AND OPPORTUNITIES IN DIABETES SELF-MANAGEMENT EDUCATION AND SUPPORT: THE ANALYSES OF DIABETES MOBILE APPLICATIONS AND PROVIDER DOCUMENTATION PATTERNS
Diabetes mellitus is one of the most prevalent chronic diseases in the United States. As a disease with long-term complications requiring changes in management, diabetes requires not only education at the time of diagnosis, but ongoing diabetes self-management education. The goal of this dissertation is to identify challenges and opportunities in diabetes self-management education and support through the analyses of diabetes mobile applications and provider documentation patterns. This dissertation includes three specific areas. First, we compared features of current diabetes mobile apps to the American Association of Diabetes Educators Self-Care BehaviorsTM guidelines. A multidisciplinary team analyzed and classified the features of…