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…
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.
Climate-driven urban heat and its adaptation at a large scale
Abstract Among many globally recognized environmental problems such as water scarcity, air pollution, and energy security, heat stress is one of the most severe climate-driven threats to the human society. The situation is further exacerbated in urban areas by urban heat islands (UHIs). Absent measures to ameliorate them, the problems associated with heat stress are expected to intensify due to rapid urban development coupled with climate change. One significant barrier to heat mitigation through urban engineering is the lack of quantitative attribution of the various surface processes toUHI intensity. In this seminar, the intrinsic mechanism of UHI and its quantitative…
DATA MINING FOR GENETIC CONTRIBUTIONS TO THE ETIOLOGY OF AUTISM SUBGROUPS
Autism is a collection complex neurological disorders characterized by behavioral, social, and cognitive deficits. Previous investigation of the etiology of autism reveals it to be a complex disorder with no simple way to identify its root cause in most affected individuals. The difficulty determining causal variation leads to the hypothesis that multiple genetic risk factors are necessary in combination to produce the autistic phenotype. Furthermore, the immense phenotypic heterogeneity seen in autism patients leads to a second hypothesis that there exist multiple subtypes of autism with distinct genetic etiologies. We developed new methods combining strategies from bioinformatics, data science, and…
