Events

Seminar Series

Presenter:

Nishant Jain

Date:

12-05-2019

Time:

3:30pm-4:30pm

Location:

Leadership Auditorium

Evaluation of chronic disease education & health information quality using online social networks & communities

Chronic diseases such as diabetes, cancer and mental illness are the leading causes of morbidity and disability. The total cost in the United States was $327 billion for diagnosed diabetes, 80.2 billion for cancer and $193.2 billion for serious mental illness. Chronic diseases rely a great deal on patient education and self-management and social media is an important tool for information dissemination in that regard.  Diabetes, cancer and mental illness are among the top 10 searched diseases on social media, which is among the newly emerging Consumer to Consumer (C2C) tools. For instance, Twitter is increasingly becoming a space for online conversations about chronic diseases, including healthy behaviors, drugs & treatments. This study hypothesizes that public perception about health topics can be influenced by the quality of social networks around a topic. A combination of methods will be used to analyze online C2C channels such as social media and publicly available health information resources. We will use various search themes based on medical topics including but not limited to emerging behavioral guidelines and standard terminology for chronic diseases. The primary goal is to visualize the nature and shape of communities and find cues about information sharing behaviors among online chronic disease communities. The hope is that this information can be exploited for educational, promotional or interventional strategies and policies. The eventual goal is to develop an informatics framework to systematically determine the quality and reliability of health information on specific medical topics.

MUIDSI Comprehensive Exam

Presenter:

Tim Haithcoat

Date:

11-08-2019

Time:

3:15PM-4:15PM

Location:

240 Naka Hall

Geospatial Context Big Table (GeoCBT): Facilitating Geospatial Analysis in Health Research

A consistent finding across health literature is that location-matters. Cancer incidence varies across scales from blocks to neighborhoods to regions. As well, a complex myriad of factors that can affect health outcomes also exists. In rural contexts, aging populations, health care access, sparse populations, environmental exposures, and infrastructure are components. In urban contexts, food-deserts, stress, and pollution (air, water, light, and noise) play possible roles. What is the interaction of all these factors? At what scale(s) is the context and association important? The collection, integration, and use of varied data are foundational to health research. However, time is wasted and effort duplicated by compiling, re-formatting, and integrating the same public sources of information at various geographic levels. In this intervening time, diseases continue to flourish and lives are potentially lost.
Geographic context is an integral component of health research. It is paramount to understand the nature of the ‘environment’ in which individuals are located in order to explore the ways that race, ethnicity, accessibility, contaminants, or other contextual characteristics affect disease incidence and outcomes. This project focuses on benefits of developing a Geospatial Context Big Table (GeoCBT) with 318 million systematic locations (rows), each with a myriad of attributes compiled from public data sources across multiple scales, geographies, and times in a queriable spatial context. There are potentially tens of thousands of attributes (columns) containing functional sociodemographic, environmental, infrastructure, cultural, economic, as well as geospatially derived data (isolation, accessibility, etc.) to provide richer context.
The ability to integrate health research data and information with spatially enabled big data within a common framework has the potential to transform health research. The GeoCBT can catalyze complex health research, broaden geospatial data use and analytics, and enable more cost-effective research.Tim Haith

Seminar Series

Presenter:

Qing Ye

Date:

10-24-2019

Time:

4:00pm-4:30PM

Location:

Leadership Auditorium, 2501 Student Center

Evaluation of Provider Documentation Patterns as a Tool to Deliver Ongoing Patient-Centered Diabetes Education and Support

Diabetes is one of the most common chronic diseases in the world. As a disease with long term complications requiring changes in management, it requires ongoing diabetes self-management education and support (DSME/S). In the United States, however, only a small proportion of people with diabetes receive DSME/S. The diabetes education that providers deliver during follow up visits may be an important source for DSME/S. We collected 200 clinic notes for 100 adults with diabetes and studied the History of Present Illness (HPI) and Impression and Plan (I&P) sections. Using a codebook based on the seven principles of American Association of Diabetes Educators Self-Care Behaviors (AADE7), we conducted a multi-step deductive thematic analysis to determine the patterns of DSME/S information occurrence in clinic notes. We used the Generalized Linear Mixed Models for investigating whether providers delivered DSME/S to people with diabetes based on patient characteristics. During follow up visits, Monitoring was the most common self-care behavior mentioned in both HPI and I&P sections. Being Active was the least common self-care behavior mentioned in HPI section and Healthy Coping was the least common self-care behavior mentioned in I&P section. Generally, providers delivered DSME/S to people with diabetes regardless of patient characteristics. The results may indicate a lack of patient-centered education when people with diabetes visit providers for ongoing management. Further studies are needed to identify the underlying reasons why providers have difficulty delivering patient-centered education.

Seminar Series

Presenter:

Zainab Al-Taie

Date:

10-24-2019

Time:

3:30pm-4:00PM

Location:

Leadership Auditorium, 2501 Student Center

Drug Repositioning for Subgroup discovery and Precision Medicine Implementation

Drug discovery is a high-cost, time-consuming, and labor-intensive process. With the declined approval rate for new drugs by the FDA, developing drug repositioning frameworks becomes crucial for improving patient care. Drug repositioning, known as old drugs for new uses, is an effective strategy to find new indications for existing drugs and is highly efficient, low-cost, and less risk. The proposed work is a network-based computational approach for subgroup cohort drug repositioning. Precision medicine is getting more attention to be applied in today’s healthcare system toward a more patient-centered system rather than a disease-based one. The phenotypic and genotypic variation among patients with the same disease required the drug discovery process, including drug repositioning, to be directed more to address the need for precision medicine implementation. In our work, we develop an exploratory data mining approach for subgroup discovery in the context of drug repositioning. A heterogeneous drug knowledge base with TCGA data was used to apply our method. For each subgroup, our approach results in a network with heterogeneous biomedical components, including different drugs that are more related to a given subgroup. Colon adenocarcinoma (COAD) samples was used as a test study. Our method has resulted in 24 subgroups with 17 candidate drugs for repositioning. We found that most of these drugs is prescribed for colon cancer, repositioning to treat this disease, or have the potential to be repurposed for COAD based on literature.

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