Machine-Learning-based Prediction of Sepsis Events from Vertical Clinical Trial Data: a Naïve Approach

Sepsis is a potentially life-threatening condition characterized by a dysregulated, disproportionate immune response to infection by which the afflicted body attacks its own tissues, sometimes to the point of organ failure, and in the worst cases, death. According to the Centers for Disease Control and Prevention (CDC) Sepsis is reported to kill upwards of 270,000 Americans annually, though this figure may be greater given certain ambiguities in the current accepted diagnostic framework of the disease. This study attempted to first establish an understanding of past definitions of sepsis, and to then recommend use of machine learning as integral in an eventual amended disease definition. Longitudinal clinical trial data (ntrials=30,915) were vectorized into a machine-readable format compatible with predictive modeling, selected and reduced in dimension, and used to predict incidences of sepsis via application of several machine learning models: logistic regression, support vector machines (SVM), naïve Bayes Classifier, decision trees, and random forests. The intent of the study was to identify possible predictive features for sepsis via comparative analysis of different machine learning models, and to recommend subsequent study of sepsis prediction using the training model on new data (non-clinical-trial-derived) in the same format. If the models can be generalized to new data, it stands to assume they could eventually become clinically useful. In referencing F1 scores and recall scores, the random forest classifier was the best performer among this cohort of models

Exposure to Classroom Poverty and Test Score Achievement: Contextual Effects or Selection?

It is widely believed that impoverished contexts harm children. Disentangling the effects of family background from the effects of other social contexts, however, is complex, making causal claims difficult to verify. This study examines the effect of exposure to classroom poverty on student test achievement using data on a cohort of children followed from third through eighth grade. Cross-sectional methods reveal a substantial negative association between exposure to high-poverty classrooms and test scores; this association grows with grade level, becoming especially large for middle school students. Growth models, however, produce much smaller effects of classroom poverty exposure on academic achievement. Even smaller effects emerge from student fixed-effects models that control for time-invariant unobservables and from marginal structural models that adjust for observable time-dependent confounding. These findings suggest that causal claims about the effects of classroom poverty exposure on achievement may be unwarranted

Accountability Pressure, Academic Standards, and Educational Triage

Despite common conceptions, evidence on whether No Child Left Behind (NCLB) has had adverse effects for low achieving students is mixed. We hypothesize that the incentive to shift attention away from the lowest achieving students increases with the rigor of state standards. Using panel data from students in North Carolina, we exploit two natural experiments: increases in the rigor of standards in math in 2006 and then again in reading in 2008. We report an increase in test score gaps between low and high achievers and students near grade level. Adverse effects on low achievers are largest in the lowest achieving schools. We discuss the policy implications of our findings given the widespread adoption of more rigorous Common Core Standards

What's in a relationship?: testing theories of social capital using data from mentoring relationships

Using data from the Big Brothers/Big Sisters program, I test multiple theories of social capital to determine which aspects of social capital lead to greater educational outcomes in a dyadic relationship. The results indicate that time spent together is the most significant and positive indicator, and is moderated by racial homogeneity of the dyad

A Matter of Degrees: Educational Credentials and Race and Gender Discrimination in the Labor Market

Racial and gender inequality in economic outcomes, particularly among the college educated, persists throughout U.S. society. Scholars debate whether this inequality stems from differences in human capital (e.g. college selectivity, GPA, major) or employer discrimination on the basis of race and gender. However, limited measures of human capital and the inherent difficulties in measuring discrimination using observational data make determining the cause of these differences in labor market outcomes a difficult endeavor. This research examines employment opportunities for hypothetical graduates of elite top-ranked universities versus less selective institutions. I use an experimental computerized audit design to create matched candidate pairs and apply for 1,008 jobs on a national job search website. The results show that although a credential from an elite university results in more call-backs for all candidates, black candidates from elite universities only do as well as white candidates from less selective universities. Moreover, race results in a double penalty: when employers respond to black candidates it is for jobs with lower starting salaries and of lower quality than those of white peers. These racial differences in response rates and starting salary ranges suggest that a bachelor's degree, even one from an elite institution, cannot fully counteract the importance of race in U.S. society. Although gender differences are not statistically significant, race and gender interact to create a tiered system of opportunities. Finally, the results suggest that college major selection plays a critical role for black but not female candidates. Overall this research finds that both racial discrimination and differences in human capital contribute to economic inequality.Doctor of Philosoph

Project Zeus: Design of a Broadband Network and its Application on a University Campus

This is a report of the results of the initial step in a plan for the design, deployment and operation of a high speed campus network at Washington University. The network is based on ATM switching technology that has been developed here during the last several years. This network will support ubiquitous multimedia workstations with high-resolution graphics and video capabilities, open up a wide range of new applications in research and education. It will support aggregate throughputs of hundreds of gigabits per second and will be designed to support port of 100 MB/s is now in operation. The next phase of network implementation will operate at 155 Mb/s port rates, with higher rates introduced as the demand arises and as economics permits. We propose to move this technology quickly into a production setting where the objectives of network use and network research can be pursued concurrently

Mojave remote sensing field experiment

The Mojave Remote Sensing Field Experiment (MFE), conducted in June 1988, involved acquisition of Thermal Infrared Multispectral Scanner (TIMS); C, L, and P-band polarimetric radar (AIRSAR) data; and simultaneous field observations at the Pisgah and Cima volcanic fields, and Lavic and Silver Lake Playas, Mojave Desert, California. A LANDSAT Thematic Mapper (TM) scene is also included in the MFE archive. TM-based reflectance and TIMS-based emissivity surface spectra were extracted for selected surfaces. Radiative transfer procedures were used to model the atmosphere and surface simultaneously, with the constraint that the spectra must be consistent with field-based spectral observations. AIRSAR data were calibrated to backscatter cross sections using corner reflectors deployed at target sites. Analyses of MFE data focus on extraction of reflectance, emissivity, and cross section for lava flows of various ages and degradation states. Results have relevance for the evolution of volcanic plains on Venus and Mars

mHealth Video Gaming For Human Papillomavirus Vaccination Among College Men-Qualitative Inquiry For Development

Background: Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States. Persistent infection with HPV can cause various cancers; however, HPV vaccination can prevent infections associated with high risk, cancerous strains of the virus. As it relates to HPV, college age men have been identified as one of the catch-up vaccination groups. Among college age men, gaming is an extremely popular extracurricular activity. Further, video games have emerged as a popular public health intervention tool. Therefore, this study aims to collect qualitative data on how to develop, implement and evaluate the effectiveness of a gaming intervention to increase HPV risk perceptions, improve self-efficacy and increase intention to receive the HPV vaccine among male college students (18–26 years old). Methods: Four focus group sessions ranging from eight to ten individuals were conducted among male college students from one large research-intensive university in the South. Using grounded theory, data from focus group interviews were coded using NVivo software to identify emergent themes. Results: Participants emphasized that although customization was not viewed as important by college aged males, the ability to tailor in game experiences or experience different things each time they played (creative freedom) was more important. They encouraged that the digital game be created on a mobile platform, incorporate health messages, and be informative to reach their population. Furthermore, they suggested innovative way to disseminate the game, which included having health department/health care providers prescribe the game to patients as an end of clinical interaction strategy. Conclusions: College age men, are natural avid gamers, enjoy game play, and can engage in learning online or offline. While platform preference varies among gamer type, college age men in our study emphasized that mobile based gaming is the most advantageous way to increase knowledge/awareness and encourage positive in game behavior which can impact out of game behaviors such as vaccination. Because of the level of access and natural disposition of mHealth technology seen as an “extension of the self”, games for health developers should consider the mobile platform as the ideal for the target demographic

GAWMerge expands GWAS sample size and diversity by combining array-based genotyping and whole-genome sequencing

Genome-wide association studies (GWAS) have made impactful discoveries for complex diseases, often by amassing very large sample sizes. Yet, GWAS of many diseases remain underpowered, especially for non-European ancestries. One cost-effective approach to increase sample size is to combine existing cohorts, which may have limited sample size or be case-only, with public controls, but this approach is limited by the need for a large overlap in variants across genotyping arrays and the scarcity of non-European controls. We developed and validated a protocol, Genotyping Array-WGS Merge (GAWMerge), for combining genotypes from arrays and whole-genome sequencing, ensuring complete variant overlap, and allowing for diverse samples like Trans-Omics for Precision Medicine to be used. Our protocol involves phasing, imputation, and filtering. We illustrated its ability to control technology driven artifacts and type-I error, as well as recover known disease-associated signals across technologies, independent datasets, and ancestries in smoking-related cohorts. GAWMerge enables genetic studies to leverage existing cohorts to validly increase sample size and enhance discovery for understudied traits and ancestries

Integration of evidence across human and model organism studies: A meeting report.

The National Institute on Drug Abuse and Joint Institute for Biological Sciences at the Oak Ridge National Laboratory hosted a meeting attended by a diverse group of scientists with expertise in substance use disorders (SUDs), computational biology, and FAIR (Findability, Accessibility, Interoperability, and Reusability) data sharing. The meeting\u27s objective was to discuss and evaluate better strategies to integrate genetic, epigenetic, and \u27omics data across human and model organisms to achieve deeper mechanistic insight into SUDs. Specific topics were to (a) evaluate the current state of substance use genetics and genomics research and fundamental gaps, (b) identify opportunities and challenges of integration and sharing across species and data types, (c) identify current tools and resources for integration of genetic, epigenetic, and phenotypic data, (d) discuss steps and impediment related to data integration, and (e) outline future steps to support more effective collaboration-particularly between animal model research communities and human genetics and clinical research teams. This review summarizes key facets of this catalytic discussion with a focus on new opportunities and gaps in resources and knowledge on SUDs