Engaging end-user driven recommender systems: personalization through web augmentation

In the past decades recommender systems have become a powerful tool to improve personalization on the Web. Yet, many popular websites lack such functionality, its implementation usually requires certain technical skills, and, above all, its introduction is beyond the scope and control of end-users. To alleviate these problems, this paper presents a novel tool to empower end-users without programming skills, without any involvement of website providers, to embed personalized recommendations of items into arbitrary websites on client-side. For this we have developed a generic meta-model to capture recommender system configuration parameters in general as well as in a web augmentation context. Thereupon, we have implemented a wizard in the form of an easy-to-use browser plug-in, allowing the generation of so-called user scripts, which are executed in the browser to engage collaborative filtering functionality from a provided external rest service. We discuss functionality and limitations of the approach, and in a study with end-users we assess the usability and show its suitability for combining recommender systems with web augmentation techniques, aiming to empower end-users to implement controllable recommender applications for a more personalized browsing experience.Fil: Wischenbart, Martin. Johannes Kepler University Linz; AustriaFil: Firmenich, Sergio Damian. Universidad Nacional de La Plata. Facultad de Informática. Laboratorio de Investigación y Formación en Informática Avanzada; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Rossi, Gustavo Héctor. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Bosetti, Gabriela Alejandra. Universidad Nacional de La Plata. Facultad de Informática; Argentina. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Conicet - La Plata; ArgentinaFil: Kapsammer, Elisabeth. Johannes Kepler University Linz; Austri

Rapid Diagnostic Algorithms as a Screening Tool for Tuberculosis: An Assessor Blinded Cross-Sectional Study

Background: A major obstacle to effectively treat and control tuberculosis is the absence of an accurate, rapid, and low-cost diagnostic tool. A new approach for the screening of patients for tuberculosis is the use of rapid diagnostic classification algorithms. Methods: We tested a previously published diagnostic algorithm based on four biomarkers as a screening tool for tuberculosis in a Central European patient population using an assessor-blinded cross-sectional study design. In addition, we developed an improved diagnostic classification algorithm based on a study population at a tertiary hospital in Vienna, Austria, by supervised computational statistics. Results: The diagnostic accuracy of the previously published diagnostic algorithm for our patient population consisting of 206 patients was 54% (CI: 47%–61%). An improved model was constructed using inflammation parameters and clinical information. A diagnostic accuracy of 86% (CI: 80%–90%) was demonstrated by 10-fold cross validation. An alternative model relying solely on clinical parameters exhibited a diagnostic accuracy of 85% (CI: 79%–89%). Conclusion: Here we show that a rapid diagnostic algorithm based on clinical parameters is only slightly improved by inclusion of inflammation markers in our cohort. Our results also emphasize the need for validation of new diagnostic algorithms in different settings and patient populations

Type of confirmation of active tuberculosis.

1<p>classification into one category based on hierarchical evidence: culture, PCR, histology, microscopy, clinical prove;</p>2<p>with adequate response to therapy, PCR =  Polymerase Chain Reaction, IGRA =  Interferon Gamma Release Assay.</p

Diagnostic performance of tested diagnostic algorithms.

<p>AUC-ROC = Area under the Receiver Operation Characteristic curve; pos = positive, neg = negative.</p><p>95% confidence intervals are computed according binominal formula of Clopper and Pearson <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0049658#pone.0049658-Clopper1" target="_blank">[44]</a>.</p>1,2<p>N = 205;</p>3,4<p>N = 187, 18 patients excluded due to extrapulmonary TB;</p>5<p>N = 205, with discretization, including: age, body mass index, C-reactive protein, night sweat;</p>6<p>N = 205,with discretization, principal components analysis; including: age, body mass index, C-reactive protein, night sweat;</p>7<p>N = 205, with discretization, principal components analysis; including: age, body mass index, night sweat;</p>8<p>N = 205, with normalization, 4 hidden layer; including: age, body mass index, night sweat;</p

Baseline characteristics of study population.

<p>FUO =  fever of unknown origin, TB =  tuberculosis.</p

Clinical and laboratory characteristics of tuberculosis and non-tuberculosis patients.

1<p>Pearsońs χ²-test, nominal scale: yes or no.</p>2<p>U-test, continuous scale.</p>3<p>BSR: blood sedimentation rate.</p>*<p>Statistically significant after adjusting for multiple testing by Bonferroni-Holm correction.</p>**<p>typical analytical sensitivity-lower boundary (test kit lot depending), n.l. =  no limit.</p