Emailing Test Results to Patients-Results Friedman, Ellen M
JAMA : the journal of the American Medical Association,
2016-Sep-27, Volume:
316, Issue:
12
Journal Article
Learning in nonstationary environments, also known as learning concept drift, is concerned with learning from data whose statistical characteristics change over time. Concept drift is further ...complicated if the data set is class imbalanced. While these two issues have been independently addressed, their joint treatment has been mostly underexplored. We describe two ensemble-based approaches for learning concept drift from imbalanced data. Our first approach is a logical combination of our previously introduced Learn++.NSE algorithm for concept drift, with the well-established SMOTE for learning from imbalanced data. Our second approach makes two major modifications to Learn++.NSE-SMOTE integration by replacing SMOTE with a subensemble that makes strategic use of minority class data; and replacing Learn++.NSE and its class-independent error weighting mechanism with a penalty constraint that forces the algorithm to balance accuracy on all classes. The primary novelty of this approach is in determining the voting weights for combining ensemble members, based on each classifier's time and imbalance-adjusted accuracy on current and past environments. Favorable results in comparison to other approaches indicate that both approaches are able to address this challenging problem, each with its own specific areas of strength. We also release all experimental data as a resource and benchmark for future research.
Chain and multi-recipient e-mails pose significant security and privacy threats such as phishing and the spread of Trojan horses. They also increase the chances of receiving spam e-mails. E-mails ...sent to multiple recipients at a time result in unwanted exposure of e-mail address to multiple recipients. The recipients of chain e-mails may include spammers or e-mail addresses of users whose e-mail account or device may have been compromised, thereby, exposing all email addresses to spammers. Forwarding or sending a multirecipient e-mail in a chain further increases the exposure of email addresses to spammers. This paper discusses chain emails, multi-recipient e-mails and crucial security and privacy threats they pose to legitimate e-mail user. It also discusses various possible mechanisms to mitigate these threats and investigates their effectiveness. This study proposes a novel technique to counter these security risks by enhancing the default behaviour of e-mail client, SMPT server and SMTP protocol. The proposed technique has been implemented in the Java programming language which showed promising results against unnecessary exposure of multiple e-mail addresses while sending an e-mail to multiple recipients. Index Terms--electronic mail, information security, privacy, unified messaging, unsolicited electronic mail.
The explosion of modeling complex systems using attributed networks boosts the research on anomaly detection in such networks, which can be applied in various high-impact domains. Many existing ...attempts, however, do not seriously tackle the inherent multi-view property in attribute space but concatenate multiple views into a single feature vector, which inevitably ignores the incompatibility between heterogeneous views caused by their own statistical properties. Actually, the distinct but complementary information brought by multi-view data promises the potential for more effective anomaly detection than the efforts only based on single-view data. Furthermore, the abnormal patterns naturally behave diversely in different views, which coincides with people's desire to discover specific abnormality according to their preferences for views (attributes). Most existing methods cannot adapt to people's requirements as they fail to consider the idiosyncrasy of user preferences. Therefore, we propose a multi-view framework Alarm to incorporate user preferences into anomaly detection and simultaneously tackle heterogeneous attribute characteristics through multiple graph encoders and a well-designed aggregator that supports self-learning and user-guided learning. Experiments on synthetic and real-world datasets, e.g., Disney, Books, and Enron, corroborate the improvement of Alarm in detection accuracy evaluated by the AUC metric and its effectiveness in supporting user-oriented anomaly detection.
Recently, the problem of boundary stabilization for unstable linear constant-coefficient coupled reaction-diffusion systems was solved by means of the backstepping method. The extension of this ...result to systems with advection terms and spatially-varying coefficients is challenging due to complex boundary conditions that appear in the equations verified by the control kernels. In this technical note we address this issue by showing that these equations are essentially equivalent to those verified by the control kernels for first-order hyperbolic coupled systems, which were recently found to be well-posed. The result therefore applies in this case, allowing us to prove H 1 stability for the closed-loop system. It also unveils a previously unknown connection between backstepping kernels for coupled parabolic and hyperbolic problems.
With the advent of 5G, cyber-physical systems (CPSs) employed in the vertical industries and critical infrastructures will depend on the cellular network more than ever; making their attack surface ...wider. Hence, guarding the network against cyberattacks is critical not only for its primary subscribers but to prevent it from being exploited as a proxy to attack CPSs. In this article, we propose a consolidated framework, by utilizing deep convolutional neural networks (CNNs) and real network data, to provide early detection for distributed denial-of-service (DDoS) attacks orchestrated by a botnet that controls malicious devices. These puppet devices individually perform silent call, signaling, SMS spamming, or a blend of these attacks targeting call, Internet, SMS, or a blend of these services, respectively, to cause a collective DDoS attack in a cell that can disrupt CPSs' operations. Our results demonstrate that our framework can achieve higher than 91\% normal and underattack cell detection accuracy.
Web- or app-based digital health studies allow for more efficient collection of health data for research. However, remote recruitment into digital health studies can enroll nonrepresentative study ...samples, hindering the robustness and generalizability of findings. Through the comprehensive evaluation of an email-based campaign on recruitment into the Health eHeart Study, we aim to uncover key sociodemographic and clinical factors that contribute to enrollment.
This study sought to understand the factors related to participation, specifically regarding enrollment, in the Health eHeart Study as a result of a large-scale remote email recruitment campaign.
We conducted a cohort analysis on all invited University of California, San Francisco (UCSF) patients to identify sociodemographic and clinical predictors of enrollment into the Health eHeart Study. The primary outcome was enrollment, defined by account registration and consent into the Health eHeart Study. The email recruitment campaign was carried out from August 2015 to February 2016, with electronic health record data extracted between September 2019 and December 2019.
The email recruitment campaign delivered at least 1 email invitation to 93.5% (193,606/206,983) of all invited patients and yielded a 3.6% (7012/193,606) registration rate among contacted patients and an 84.1% (5899/7012) consent rate among registered patients. Adjusted multivariate logistic regression models analyzed independent sociodemographic and clinical predictors of (1) registration among contacted participants and (2) consent among registered participants. Odds of registration were higher among patients who are older, women, non-Hispanic White, active patients with commercial insurance or Medicare, with a higher comorbidity burden, with congestive heart failure, and randomized to receive up to 2 recruitment emails. The odds of registration were lower among those with medical conditions such as dementia, chronic pulmonary disease, moderate or severe liver disease, paraplegia or hemiplegia, renal disease, or cancer. Odds of subsequent consent after initial registration were different, with an inverse trend of being lower among patients who are older and women. The odds of consent were also lower among those with peripheral vascular disease. However, the odds of consent remained higher among patients who were non-Hispanic White and those with commercial insurance.
This study provides important insights into the potential returns on participant enrollment when digital health study teams invest resources in using email for recruitment. The findings show that participant enrollment was driven more strongly by sociodemographic factors than clinical factors. Overall, email is an extremely efficient means of recruiting participants from a large list into the Health eHeart Study. Despite some improvements in representation, the formulation of truly diverse studies will require additional resources and strategies to overcome persistent participation barriers.
Convolutional neural networks (CNNs) have recently achieved remarkable successes in various image classification and understanding tasks. The deep features obtained at the top fully connected layer ...of the CNN (FC-features) exhibit rich global semantic information and are extremely effective in image classification. On the other hand, the convolutional features in the middle layers of the CNN also contain meaningful local information, but are not fully explored for image representation. In this paper, we propose a novel locally supervised deep hybrid model (LS-DHM) that effectively enhances and explores the convolutional features for scene recognition. First, we notice that the convolutional features capture local objects and fine structures of scene images, which yield important cues for discriminating ambiguous scenes, whereas these features are significantly eliminated in the highly compressed FC representation. Second, we propose a new local convolutional supervision layer to enhance the local structure of the image by directly propagating the label information to the convolutional layers. Third, we propose an efficient Fisher convolutional vector (FCV) that successfully rescues the orderless mid-level semantic information (e.g., objects and textures) of scene image. The FCV encodes the large-sized convolutional maps into a fixed-length mid-level representation, and is demonstrated to be strongly complementary to the high-level FC-features. Finally, both the FCV and FC-features are collaboratively employed in the LS-DHM representation, which achieves outstanding performance in our experiments. It obtains 83.75% and 67.56% accuracies, respectively, on the heavily benchmarked MIT Indoor67 and SUN397 data sets, advancing the state-of-the-art substantially.
This technical note deals with a modified algebraic Riccati equation (MARE) and its corresponding inequality and difference equation, which arise in modified optimal control and filtering problems ...and are introduced into the cooperative control problems recently. The stabilizing property of the solution to MARE is presented. Then, the uniqueness is proved for the almost stabilizing and positive semi-definite solution. Next, the parameter dependence of MARE is analyzed. An obtained parameter dependence result is finally applied to the study of semi-global synchronization of leader-following networks with discrete-time linear dynamics subject to actuator saturation.
Recent work has shown that when both the chart and caption emphasize the same aspects of the data, readers tend to remember the doubly-emphasized features as takeaways; when there is a mismatch, ...readers rely on the chart to form takeaways and can miss information in the caption text. Through a survey of 280 chart-caption pairs in real-world sources (e.g., news media, poll reports, government reports, academic articles, and Tableau Public), we find that captions often do not emphasize the same information in practice, which could limit how effectively readers take away the authors' intended messages. Motivated by the survey findings, we present E MPHASISCHECKER , an interactive tool that highlights visually prominent chart features as well as the features emphasized by the caption text along with any mismatches in the emphasis. The tool implements a time-series prominent feature detector based on the Ramer-Douglas-Peucker algorithm and a text reference extractor that identifies time references and data descriptions in the caption and matches them with chart data. This information enables authors to compare features emphasized by these two modalities, quickly see mismatches, and make necessary revisions. A user study confirms that our tool is both useful and easy to use when authoring charts and captions.
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