A tandem evolutionary algorithm for identifying causal rules from complex data

We propose a new evolutionary approach for discovering causal rules in complex classification problems from batch data. Key aspects include (a) the use of a hypergeometric probability mass function as a principled statistic for assessing fitness that quantifies the probability that the observed association between a given clause and target class is due to chance, taking into account the size of the dataset, the amount of missing data, and the distribution of outcome categories, (b) tandem age-layered evolutionary algorithms for evolving parsimonious archives of conjunctive clauses, and disjunctions of these conjunctions, each of which have probabilistically significant associations with outcome classes, and (c) separate archive bins for clauses of different orders, with dynamically adjusted order-specific thresholds. The method is validated on majority-on and multiplexer benchmark problems exhibiting various combinations of heterogeneity, epistasis, overlap, noise in class associations, missing data, extraneous features, and imbalanced classes. We also validate on a more realistic synthetic genome dataset with heterogeneity, epistasis, extraneous features, and noise. In all synthetic epistatic benchmarks, we consistently recover the true causal rule sets used to generate the data. Finally, we discuss an application to a complex real-world survey dataset designed to inform possible ecohealth interventions for Chagas disease

Referral Patterns Between Allopathic Physicians and Complementary and Alternative Medicine Practitioners: A Followup Study

Introduction: • Despite the high prevalence of Complementary and Alternative Medicine (CAM) usage, several recent surveys suggest that the vast majority of patient visits to CAM practitioners are self-referred and that communication between conventional and CAM practitioners is limited. • There is a need for a better understandingof factors influencing referral patterns across these two groups of practitioners. • Network analysis provides a useful tool to quantify relationships between members of an interrelated social network. • The goal of this follow up study was to quantify the cross-class referral patterns between conventional and CAM classes of practitioners in Chittenden County Vermont as well as gather additional information on the basis of referrals for future studies. • This study was a preliminary examination of possible reasons for the referral patterns.https://scholarworks.uvm.edu/comphp_gallery/1039/thumbnail.jp

Analysis of a consumer survey on plug-in hybrid electric vehicles

Plug-in Hybrid Electric Vehicles (PHEVs) show potential to reduce greenhouse gas (GHG) emissions, increase fuel efficiency, and offer driving ranges that are not limited by battery capacity. However, these benefits will not be realized if consumers do not adopt this new technology. Several agent-based models have been developed to model potential market penetration of PHEVs, but gaps in the available data limit the usefulness of these models. To address this, we administered a survey to 1000 stated US residents, using Amazon Mechanical Turk, to better understand factors influencing the potential for PHEV market penetration. Our analysis of the survey results reveals quantitative patterns and correlations that extend the existing literature. For example, respondents who felt most strongly about reducing US transportation energy consumption and cutting greenhouse gas emissions had, respectively, 71 and 44 times greater odds of saying they would consider purchasing a compact PHEV than those who felt least strongly about these issues. However, even the most inclined to consider a compact PHEV were not generally willing to pay more than a few thousand US dollars extra for the sticker price. Consistent with prior research, we found that financial and battery-related concerns remain major obstacles to widespread PHEV market penetration. We discuss how our results help to inform agent-based models of PHEV market penetration, governmental policies, and manufacturer pricing and marketing strategies to promote consumer adoption of PHEVs. © 2014 The Authors

Referral Patterns Between Allopathic Physicians and Complementary and Alternative Medicine Practitioners

Introduction and Objectives: The provision of basic healthcare in the United States may be viewed considering two different, and sometimes combined, therapeutic approaches: •Allopathic/osteopathic medicine •Complementary and alternative medicine (CAM) Our study is interested in the intersection of allopathic medicine and CAM. Evidence suggests that Americans are seeking CAM at a similar or even a higher rate than allopathic medicine, yet there seems to be a division between practitioners of each discipline. Isthis division created by a lack of coordination, such as an inadequately established referral system, or by a general lack of knowledge, or by the attitudes of the practitioners? In our study our objectives were: ? To assess the referral patterns between allopathic and CAM practitioners in Chittenden County. ? To examine the various factors that may influence these referral patterns using confidential surveys.https://scholarworks.uvm.edu/comphp_gallery/1017/thumbnail.jp