Identifying Patients at Risk of High Healthcare Utilization

Objective: To develop a systematic and reproducible way to identify patients at increased risk for higher healthcare costs. Methods: Medical records were analyzed for 9,581 adults who were primary care patients in the University of Missouri Health System and who were enrolled in Medicare or Medicaid. Patients were categorized into one of four risk tiers as of October 1, 2013, and the four tiers were compared on demographic characteristics, number of healthcare episodes, and healthcare charges in the year before and the year after cohort formation. Results: The mean number of healthcare episodes and the sum of healthcare charges in…

A Metagenomic Analysis of the Effect of Residual Feed Intake on Rumen Metabolism

Ruminant animals have a symbiotic relationship with gastrointestinal microorganisms in the rumen where microbes degrade compounds that can be used in the host animal’s metabolism. Currently, changes in the diet or feed efficiency of the sheep results in differences to the rumen’s microbiota population. By using a metabolic approach, the effects of differing residual feed intake (RFI) on the rumen’s microbiome are analyzed to determine the network interface between the host’s metabolism and rumen microbiome. These findings demonstrate important network structure differences between low and high RFI animals providing a greater understanding of the complexities in the rumen ecosystem.

Big Data Colloquium Distinguished Speaker – Dr. Jianjiong Gao

Thanks to the advancements of technology such as next-generation sequencing, an overwhelming amount of cancer genomics data has been generated by large-scale cancer genomics projects such as The Cancer Genome Atlas (TCGA). This has imposed an increasing challenge in the translation of the wealth of the resulting “big data” into biological discoveries and clinical applications. In this talk, I will present two major platforms we developed at Memorial Sloan Kettering Cancer Center to address this challenge: cBioPortal and OncoKB. The cBioPortal for Cancer Genomics (http://cbioportal.org/) collects, integrates, and visualizes multi-dimensional, high-level cancer genomics and clinical data. It was specifically…

Diabetes Self-Management Applications: Focus Group Findings from Elderly Diabetic Patients

The number of mobile diabetes self-management (DSM) apps has risen. However, it is not certain whether these apps provide effective DSM for elderly diabetic patients. The purpose of this study was to identify barriers in functionality and usability related to needs of elderly diabetic patients for DSM apps. We conducted two focus groups with 10 older diabetic patients. Participants completed a set of DSM tasks using nine representative DSM apps on iPads. They answered a questionnaire which included basic information, System Usability Scale (SUS), app specific questions, and open-ended questions. We found DSM apps did not adhere to diabetes guidelines.

Big Data Colloquium Distinguished Speaker – Dr. Philip S. Yu

The problem of big data has become increasingly importance in recent years. On the one hand, big data is an asset that potentially can offer tremendous value or reward to the data owners. On the other hand, it poses tremendous challenges to distil the value out of the big data. The very nature of big data poses challenges not only due to its volume, and velocity of being generated, but also its variety, where variety means the data can be collected from various sources with different formats from structured data to text to network/graph data, etc. In this talk, we…

MUII Defense Announcement – Andrew Hutson

Background: The percentage of patients with polypharmacy needs is increasing among a growing patient population. As a result, the amount of time health care professionals require to make clinical decisions based on current and past medications is increasing. Health care professionals need methods for increasing the speed of clinical decision making without sacrificing the quality of care.  The goal of this study is to demonstrate how modifying the data visualization for patient medication histories will change decision making speed or efficacy. Methods: We compared two groups across five randomized blocks. Group 1 responded to questions based on the control data…

MUII Defense Announcement – Ginger Han

Biomedical image data have been growing quickly in volume, speed, and complexity, and there is an increasing reliance on the analysis of these data. Biomedical scientists are in need of efficient and accurate analyses of large-scale imaging data, as well as innovative retrieval methods for visually similar imagery across a large-scale data collection to assist complex study in biological and medical applications. Moreover, biomedical images rely on increased resolution to capture subtle phenotypes of diseases, but this poses a challenge for clinicians to sift through haystacks of visual cues to make informative diagnoses.  To tackle these challenges, we developed computational…

Botanical treatments to inhibit endometriosis: A next generation RNA-seq data analysis

Endometriosis is a benign but complex gynecologic disorder associated with pelvic pain and infertility and is characterized by the implantation of endometrial tissue outside of the uterus. To date, the reason remains unknown, though the immune dysfunction has been implicated. Hormonal interventions are widely used as effective treatment but with undesirable side effects. Botanical treatments can be alternative options with fewer side effects. In this study, we employed RNA-seq technology to assess the effects of two different botanical treatments, quercetin, and elderberry juice over vehicle (control), to inhibit the growth of endometriotic lesions in a mouse model (C57BL/6) of endometriosis.

Mining for epigenetic patterns across species

Exponential growth of next-generation sequencing technologies has made the epigenomics analysis a big data science, which poses the challenges to its translation into knowledge. This has led to the emergence of a new field called “Comparative Epigenomics.” Comparative epigenomics has three major directions, namely comparison across species, across time-course of a biological process, and across individuals. In this study we focus on comparing the epi-modifications across species (particularly between Human and Mouse). We compared different epi-genomic factors and where/how they concur or differ among species. We have used histone modification data from various publicly available data sets in Human and…