Employment of social component in GPS navigation

GPS tehnologija je danes osnoven del vsakega potovanja, z razvojem pametnih telefonov pa je postala dostopna večjemu številu uporabnikov. Skozi čas je razvoj tehnologije omogočil večjo dostopnost različnih informacij, ki vplivajo na potovanja in na večjo povezavo med uporabniki. Dejstvo je, da so obstoječe GPS aplikacije zastarele, zaradi česar smo si v okviru magistrske naloge zastavili cilj razviti GPS aplikacijo naslednje generacije, ki bo z močno uporabo socialne komponente ter spoznavanjem svojih uporabnikov izboljšala uporabniško izkušnjo mobilnih GPS aplikacij. Sistem je zasnovan na uporabi večih metod strojnega učenja, pri katerih se morajo zaradi posebne strukture sistema te metode prilagoditi za uporabo na mobilni platformi. Na podlagi predlaganega sistema smo izdelali tudi funkcionalen prototip, ki je dokazal, da se obstoječe metode lahko uporabljajo za izboljšavo uporabniške izkušnje mobilnih aplikacij, ki temeljijo na GPS tehnologiji.GPS technology has become an essential part of any travel and, with the development of smartphone technology, it has become accessible to a larger number of users. The development of technology over time has also enabled easier access to different information which can influence travel, and users of these applications have become more connected than ever before. All of this has resulted in one simple fact - existing GPS applications have become obsolete. For this reason, we have set the goal of developing the next generation of GPS applications, which will make significant use of a social component, as well as learning about its users’ interests in order to improve the user experience. The system is designed to use different methods of machine learning, which have to be optimized for use on mobile platforms. We have also developed a functional prototype, which has demonstrated that the existing methods can be used to improve the user experience of GPS mobile applications

Cancer Pharmacogenomics and Pharmacoepidemiology: Setting a Research Agenda to Accelerate Translation

Recent advances in genomic research have demonstrated a substantial role for genomic factors in predicting response to cancer therapies. Researchers in the fields of cancer pharmacogenomics and pharmacoepidemiology seek to understand why individuals respond differently to drug therapy, in terms of both adverse effects and treatment efficacy. To identify research priorities as well as the resources and infrastructure needed to advance these fields, the National Cancer Institute (NCI) sponsored a workshop titled “Cancer Pharmacogenomics: Setting a Research Agenda to Accelerate Translation” on July 21, 2009, in Bethesda, MD. In this commentary, we summarize and discuss five science-based recommendations and four infrastructure-based recommendations that were identified as a result of discussions held during this workshop. Key recommendations include 1) supporting the routine collection of germline and tumor biospecimens in NCI-sponsored clinical trials and in some observational and population-based studies; 2) incorporating pharmacogenomic markers into clinical trials; 3) addressing the ethical, legal, social, and biospecimen- and data-sharing implications of pharmacogenomic and pharmacoepidemiologic research; and 4) establishing partnerships across NCI, with other federal agencies, and with industry. Together, these recommendations will facilitate the discovery and validation of clinical, sociodemographic, lifestyle, and genomic markers related to cancer treatment response and adverse events, and they will improve both the speed and efficiency by which new pharmacogenomic and pharmacoepidemiologic information is translated into clinical practice

The relationship between time to diagnose and diagnostic accuracy among internal medicine residents: a randomized experiment

Background: Diagnostic errors have been attributed to cognitive biases (reasoning shortcuts), which are thought to result from fast reasoning. Suggested solutions include slowing down the reasoning process. However, slower reasoning is not necessarily more accurate than faster reasoning. In this study, we studied the relationship between time to diagnose and diagnostic accuracy. Methods: We conducted a multi-center within-subjects experiment where we prospectively induced availability bias (using Mamede et al.’s methodology) in 117 internal medicine residents. Subsequently, residents diagnosed cases that resembled those bias cases but had another correct diagnosis. We determined whether residents were correct, incorrect due to bias (i.e. they provided the diagnosis induced by availability bias) or due to other causes (i.e. they provided another incorrect diagnosis) and compared time to diagnose. Results: We did not successfully induce bias: no significant effect of availability bias was found. Therefore, we compared correct diagnoses to all incorrect diagnoses. Residents reached correct diagnoses faster than incorrect diagnoses (115 s vs. 129 s, p <.001). Exploratory analyses of cases where bias was induced showed a trend of time to diagnose for bias diagnoses to be more similar to correct diagnoses (115 s vs 115 s, p =.971) than to other errors (115 s vs 136 s, p =.082). Conclusions: We showed that correct diagnoses were made faster than incorrect diagnoses, even within subjects. Errors due to availability bias may be different: exploratory analyses suggest a trend that biased cases were diagnosed faster than incorrect diagnoses. The hypothesis that fast reasoning leads to diagnostic errors should be revisited, but more research into the characteristics of cognitive biases is important because they may be different from other causes of diagnostic errors