March 9, 2020
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…
Feb. 18, 2020
Neural information extraction in biomedical domain: issues and challenges
Much medical data today remain inaccessible thus limiting their impact on patient care. Images and illustrations, scientific articles, and free-text reports do not allow for easy extraction and re-use of the knowledge they contain. They lack the structure and metadata necessary for automated processing and annotation. The resources required to collect and annotate manually are not sufficient to produce enough comprehensive benchmark datasets to bootstrap specialty research. We discuss neural network-based approaches to the problem of extraction of medical information from clinical images and unstructured text sources.
Feb. 12, 2020
Applied AI in CDSS in Medicine: A Systematic Review
Objective: Clinical decision support systems (CDSS) are continuously developing to solve medical problems and try to improve healthcare management, which has shown a significant result in reducing medical errors and improving multiple healthcare processes. These days, artificial intelligence (AI) becomes more influential in healthcare supporting physicians to make a clinical decision. Materials and Methods: A systematic review was conducted to identify articles related to CDSS using AI algorithms. The original research was published between 2009 and 2019 in the English language. In a total of 3,687 identified articles, 1,112 articles were analyzed, and 199 articles are represented within this review.
Feb. 12, 2020
Contrast Data Mining and Pattern Discovery for Glaucoma Risk Assessments
Glaucoma is the second leading cause of irreversible blindness across the world, about 70 million people have glaucoma, and 4.4 million people are blind due to undiagnosed glaucoma by optic nerve damage worldwide. Studies show that early prediction is the best way to prevent irreversible blindness. To address this problem, we applied a subgroup contrast set mining for glaucoma risk assessment. Contrast mining has been successfully applied in health care data analytics and demonstrated in recent work from our lab using a large volume of EHR (Electronic health records) data analysis. The main goal of this method is to identify…
Dec. 4, 2019
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…
Nov. 15, 2019
The power and challenges of single cell gene expression technology
For many common diseases, a significant percentage of patients simply do not respond effectively or have adverse side-effects to any given treatment. To more successfully focus our disease intervention targets, comparatively diverse species with high-resolution starting points are required. An understanding of cell type–specific variation in gene expression is key to deciphering the roles of genes in disease. Accordingly, newly developed single-cell methods have started to fill our gaps in knowledge of disease by allowing us to peer deeply into the molecular microenvironments of tissues and organs. Multiple lipid emulsion techniques for isolating and evaluating single cells have been developed;…
Nov. 5, 2019
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…
Oct. 23, 2019
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…
Oct. 23, 2019
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…