Quantitatively Motivated Model Development Framework: Downstream Analysis Effects of Normalization Strategies

Through a review of epistemological frameworks in social sciences, history of frameworks in statistics, as well as the current state of research, we establish that there appears to be no consistent, quantitatively motivated model development framework in data science, and the downstream analysis effects of various modeling choices are not uniformly documented. Examples are provided which illustrate that analytic choices, even if justifiable and statistically valid, have a downstream analysis effect on model results. This study proposes a unified model development framework that allows researchers to make statistically motivated modeling choices within the development pipeline. Additionally, a simulation study is used to determine empirical justification of the proposed framework. This study tests the utility of the proposed framework by investigating the effects of normalization on downstream analysis results. Normalization methods are investigated by utilizing a decomposition of the empirical risk functions, measuring effects on model bias, variance, and irreducible error. Measurements of bias and variance are then applied as diagnostic procedures for model pre-processing and development within the unified framework. Findings from simulation results are included in the proposed framework and stress-tested on benchmark datasets as well as several applications

Application of Support Vector Machine Modeling and Graph Theory Metrics for Disease Classification

Disease classification is a crucial element of biomedical research. Recent studies have demonstrated that machine learning techniques, such as Support Vector Machine (SVM) modeling, produce similar or improved predictive capabilities in comparison to the traditional method of Logistic Regression. In addition, it has been found that social network metrics can provide useful predictive information for disease modeling. In this study, we combine simulated social network metrics with SVM to predict diabetes in a sample of data from the Behavioral Risk Factor Surveillance System. In this dataset, Logistic Regression outperformed SVM with ROC index of 81.8 and 81.7 for models with and without graph metrics, respectively. SVM with a polynomial kernel had ROC index of 72.9 and 75.6 for models with and without graph metrics, respectively. Although this did not perform as well as Logistic Regression, the results are consistent with previous studies utilizing SVM to classify diabetes

A Comparison of Decision Tree with Logistic Regression Model for Prediction of Worst Non-Financial Payment Status in Commercial Credit

Credit risk prediction is an important problem in the financial services domain. While machine learning techniques such as Support Vector Machines and Neural Networks have been used for improved predictive modeling, the outcomes of such models are not readily explainable and, therefore, difficult to apply within financial regulations. In contrast, Decision Trees are easy to explain, and provide an easy to interpret visualization of model decisions. The aim of this paper is to predict worst non-financial payment status among businesses, and evaluate decision tree model performance against traditional Logistic Regression model for this task. The dataset for analysis is provided by Equifax and includes over 300 potential predictors from more than 11 million unique businesses. After a data discovery phase, including imputation, cleaning, and transforming potential predictors, Decision Tree and Logistic Regression models were built on the same finalized analysis dataset. Evaluating the models based on ROC index, and Kolmogorov-Smirnov statistic, Decision Tree performed as well as the Logistic Regression model

Genetic Algorithm Guidance of a Constraint Programming Solver for the Multiple Traveling Salesman Problem

This project developed a metaheuristic approach to the Multiple Traveling Salesman Problem that pairs a custom genetic algorithm with a conventional combinatorial optimization solver. This combined approach was used to build an optimal route for two popular radio show hosts to visit each of the 37 Atlanta area Jersey Mike\u27s Subs in one day. This supported a fundraising eort to send children with chronic and terminal illnesses to Disney World through an organization called Bert\u27s Big Adventure. Atlanta-area Jersey Mike\u27s locations donated 100% of proceeds earned on this Day of Giving to Bert\u27s Big Adventure. With the suggested route developed through our approach, the radio hosts successfully visited all 37 Jersey Mike\u27s in one day, a task Bert\u27s Big Adventure staff members had not been able to complete in previous years

Carotid Atheroinflammation Is Associated With Cerebral Small Vessel Disease Severity.

Background: Atherosclerosis is a systemic inflammatory disease, with common inflammatory processes implicated in both atheroma vulnerability and blood-brain barrier disruption. This prospective multimodal imaging study aimed to measure directly the association between systemic atheroma inflammation ("atheroinflammation") and downstream chronic cerebral small vessel disease severity. Methods: Twenty-six individuals with ischemic stroke with ipsilateral carotid artery stenosis of >50% underwent 18fluoride-fluorodeoxyglucose-positron emission tomography within 2 weeks of stroke. Small vessel disease severity and white matter hyperintensity volume were assessed using 3-tesla magnetic resonance imaging also within 2 weeks of stroke. Results: Fluorodeoxyglucose uptake was independently associated with more severe small vessel disease (odds ratio 6.18, 95% confidence interval 2.1-18.2, P < 0.01 for the non-culprit carotid artery) and larger white matter hyperintensity volumes (coefficient = 14.33 mL, P < 0.01 for the non-culprit carotid artery). Conclusion: These proof-of-concept results have important implications for our understanding of the neurovascular interface and potential therapeutic exploitation in the management of systemic atherosclerosis, particularly non-stenotic disease previously considered asymptomatic, in order to reduce the burden of chronic cerebrovascular disease

In vitro pharmacology of fentanyl analogs at the human mu opioid receptor and their spectroscopic analysis

Opioids are widely misused and account for almost half of overdose deaths in the United States. The cost in terms of lives, health care, and lost productivity is significant and has been declared a national crisis. Fentanyl is a highly potent mu opioid receptor (MOR) agonist and plays a significant role in the current opioid epidemic; fentanyl and its analogs (fentalogs) are increasingly becoming one of the biggest dangers in the opioid crisis. The availability of fentalogs in the illicit market is thought to play a significant role in the recent increase in opioid‐related deaths. Although there is both rodent homolog in vivo and in vitro data for some fentalogs, prior to this publication very little was known about the pharmacology of many of these illicit compounds at the human MOR (hMOR). Using gas chromatography–mass spectrometry, nuclear magnetic resonance spectroscopy, and in vitro assays, this study describes the spectral and pharmacological properties of 34 fentalogs. The reported spectra and chemical data will allow for easy identification of novel fentalogs in unknown or mixed samples. Taken together these data are useful for law enforcement and clinical workers as they will aid in the identification of fentalogs in unknown samples and can potentially be used to predict physiological effects after exposure.This study reports the basic in vitro pharmacology (affinity, agonist activity, and potencies) of 34 fentanyl analogs at the human mu opioid receptor. In addition, these fentalogs are analyzed spectroscopically using gas chromatography–mass spectrometry and proton nuclear magnetic resonance spectroscopy, to understand structural commonalities and key differences for identification.Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/156439/2/dta2822.pdfhttp://deepblue.lib.umich.edu/bitstream/2027.42/156439/1/dta2822_am.pd

Overcoming the fear of lethal injury: Evaluating suicidal behavior in the military through the lens of the Interpersonal–Psychological Theory of Suicide

Suicide rates have been increasing in military personnel since the start of Operation Enduring Freedom and Operation Iraqi Freedom, and it is vital that efforts be made to advance suicide risk assessment techniques and treatment for members of the military who may be experiencing suicidal symptoms. One potential way to advance the understanding of suicide in the military is through the use of the Interpersonal-Psychological Theory of Suicide. This theory proposes that three necessary factors are needed to complete suicide: feelings that one does not belong with other people, feelings that one is a burden on others or society, and an acquired capability to overcome the fear and pain associated with suicide. This review analyzes the various ways that military service may influence suicidal behavior and integrates these findings into an overall framework with relevant practical implications. Findings suggest that although there are many important factors in military suicide, the acquired capability may be the most impacted by military experience because combat exposure and training may cause habituation to fear of painful experiences, including suicide. Future research directions, ways to enhance risk assessment, and treatment implications are also discussed.Psycholog

Excellent Adherence to Antiretrovirals in HIV+ Zambian Children Is Compromised by Disrupted Routine, HIV Nondisclosure, and Paradoxical Income Effects

INTRODUCTION: A better understanding of pediatric antiretroviral therapy (ART) adherence in sub-Saharan Africa is necessary to develop interventions to sustain high levels of adherence. METHODOLOGY/PRINCIPAL FINDINGS: Adherence among 96 HIV-infected Zambian children (median age 6, interquartile range [IQR] 2,9) initiating fixed-dose combination ART was measured prospectively (median 23 months; IQR 20,26) with caregiver report, clinic and unannounced home-based pill counts, and medication event monitoring systems (MEMS). HIV-1 RNA was determined at 48 weeks. Child and caregiver characteristics, socio-demographic status, and treatment-related factors were assessed as predictors of adherence. Median adherence was 97.4% (IQR 96.1,98.4%) by visual analog scale, 94.8% (IQR 86,100%) by caregiver-reported last missed dose, 96.9% (IQR 94.5,98.2%) by clinic pill count, 93.4% (IQR 90.2,96.7%) by unannounced home-based pill count, and 94.8% (IQR 87.8,97.7%) by MEMS. At 48 weeks, 72.6% of children had HIV-1 RNA <50 copies/ml. Agreement among adherence measures was poor; only MEMS was significantly associated with viral suppression (p = 0.013). Predictors of poor adherence included changing residence, school attendance, lack of HIV disclosure to children aged nine to 15 years, and increasing household income. CONCLUSIONS/SIGNIFICANCE: Adherence among children taking fixed-dose combination ART in sub-Saharan Africa is high and sustained over two years. However, certain groups are at risk for treatment failure, including children with disrupted routines, no knowledge of their HIV diagnosis among older children, and relatively high household income, possibly reflecting greater social support in the setting of greater poverty

(Re) defining salesperson motivation: current status, main challenges, and research directions

The construct of motivation is one of the central themes in selling and sales management research. Yet, to-date no review article exists that surveys the construct (both from an extrinsic and intrinsic motivation context), critically evaluates its current status, examines various key challenges apparent from the extant research, and suggests new research opportunities based on a thorough review of past work. The authors explore how motivation is defined, major theories underpinning motivation, how motivation has historically been measured, and key methodologies used over time. In addition, attention is given to principal drivers and outcomes of salesperson motivation. A summarizing appendix of key articles in salesperson motivation is provided

Effects of antiplatelet therapy on stroke risk by brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases: subgroup analyses of the RESTART randomised, open-label trial

Background Findings from the RESTART trial suggest that starting antiplatelet therapy might reduce the risk of recurrent symptomatic intracerebral haemorrhage compared with avoiding antiplatelet therapy. Brain imaging features of intracerebral haemorrhage and cerebral small vessel diseases (such as cerebral microbleeds) are associated with greater risks of recurrent intracerebral haemorrhage. We did subgroup analyses of the RESTART trial to explore whether these brain imaging features modify the effects of antiplatelet therapy