Forty-nine patients aged 65-89 years, treated for 6 months-6 years (mean 3.9 years) over a total of 195 patient years, were studied. The efficacy of anticoagulant control or the occurrence of complications or treatment failures did not vary with the age, sex, social status, mobility, visual acuity, domiciliary supervision of medication or the indication for anticoagulation. A significant correlation was observed between the concomitant drug therapy and anticoagulant control (P less than 0.001) but not with the occurrence of complications and treatment failures. Poor anticoagulant control was observed particularly in those receiving drugs known to potentiate warfarin effect and in whom more changes were made to their concomitant drug therapy. Five patients who experienced six non-fatal haemorrhages (two major and four minor) showed poor overall anticoagulant control from the outset (P less than 0.01). The treatment failure rate was 4%.
Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. Methods.We propose an application and solution approach for automatically generating and suggesting short email responses to support queries in a university environment. Our proposed solution can be used as one tap or one click solution for responding to various types of queries raised by faculty members and students in a university. We create a dataset for the application domain and make it publicly available. We apply a machine learning framework for classifying emails into categories such as office of academic affairs or information technology department. We apply a machine learning based classification approach for sub-category level classification also. We apply text pre-processing techniques, feature selection, support vector machine and naïve naive classifiers. We present an approach to overcome various natural language processing based challenges in the text. Results.We conduct a series of experiments and evaluate the approach using confusion matrix and accuracy based metrics. We study the discriminatory power of features and compare their relevance for the classification task. Our experimental results reveal that the proposed approach is effective. We conclude from our experiments that discriminatory features can be extracted from the text within our specific domain and automatic email response suggestion can be accurately created using machine learning algorithms and framework. We experiment with two different learning algorithms and observe that SVM outperforms Naïve Bayes. We achieve a classification accuracy of above $85\%$ for all the classes and sub-classes. Discussion. Our experiments on email response suggestion are conducted on a corpus consists of short and frequent emails by a university function but the proposed approach and techniques can be generalized to other domains also. We observe that different classifiers give different results and there is a significant difference in the predictive power of features. Background. Office of Academic Affairs (OAA), Office of Student Life (OSL) and Information Technology Helpdesk (ITD) are support functions within a university which receives hundreds of email messages on the daily basis. A large percentage of emails received by these departments are frequent and commonly used queries or request for information. Responding to every query by manually typing is a tedious and time consuming task and an automated approach for email response suggestion can save lot of time. Methods. We propose an application and solution approach for automatically generating an...
Users of web browsers today swim in a sea of haphazardly organized tabs, bookmarks, searches and downloads. This is because browsers operate at the level of individual web pages, and rarely understand the user's high-level tasks. We propose to address this problem by compartmentalizing the web browsing experience across different tasks defined by the user. We describe Sailboat, an extension for Google Chrome that explores this idea by making user-defined tasks first-class objects in the browsing experience. In Sailboat, different tasks have their own tabs, history, bookmarks and download folders, while maintaining a unified identity across compartments. Sailboat lets the user create and switch between tasks such as "Philosophy Paper", "Summer Internships", and "Trip to China". Users can encapsulate the browsing related to a task, archive it or share it with others, and come back to it months later. Sailboat also tracks and reflects the analytics of time spent on different tasks to aid productivity. We deployed Sailboat with several users, and studied how they engaged with it. Our users report that Sailboat aids organized browsing, reduces distractions, and is easy to get used to. CCS CONCEPTS • Information systems → Browsers; • Human-centered computing → Web-based interaction.
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