Background A computer-assisted history-taking system (CAHTS) is a tool that aids clinicians in gathering data from patients to inform a diagnosis or treatment plan. Despite the many possible applications and even though CAHTS have been available for nearly three decades, these remain underused in routine clinical practice. Objective Through an interpretative review of the literature, we provide an overview of the field of CAHTS, which also offers an understanding of the impact of these systems on policy, practice and research. Methods We conducted a search and critique of the literature on CAHTS. Using a comprehensive set of terms, we searched: MEDLINE, EMBASE, The Cochrane Database of Systematic Reviews, Database of Abstracts of Reviews of Effects, The Cochrane Central Register of Controlled Trials, The Cochrane Methodology Register, Health Technology Assessment Database and the NHS Economic Evaluation Database over a ten-year period (January 1997 to May 2007) to identify systematic reviews, technical reports and health technology assessments, and randomised controlled trials. Results The systematic review of the literature suggests that CAHTS can save professionals' time, improve delivery of care to those with special needs and also facilitate the collection of information,

The information-based prediction models using machine learning techniques have gained massive popularity during the last few decades. Such models have been applied in a number of domains such as medical diagnosis, crime prediction, movies rating, etc. Similar is the trend in telecom industry where prediction models have been applied to predict the dissatisfied customers who are likely to change the service provider. Due to immense financial cost of customer churn in telecom, the companies from all over the world have analyzed various factors (such as call cost, call quality, customer service response time, etc.) using several learners such as decision trees, support vector machines, neural networks, probabilistic models such as Bayes, etc. This paper presents a detailed survey of models from 2000 to 2015 describing the datasets used in churn prediction, impacting features in those datasets and classifiers that are used to implement prediction model. A total of 48 studies related to churn prediction in telecom industry are discussed using 23 datasets (3 public and 20 private). Our survey aims to highlight the evolution of techniques from simple features/learners to more complex learners and feature engineering or sampling techniques. We also give an overview of the current challenges in churn prediction and suggest solutions to resolve them. This paper will allow researchers such as data analysts in general and telecom operators in particular to choose best suited techniques and features to prepare their churn prediction models.

Abstract-Learning Management System (LMS) is an effective platform for communication and collaboration among teachers and students to enhance learning. These LMSs are now widely used in both conventional and virtual and distance learning paradigms. These LMSs have various limitations as identified in the existing literature, including poor learning content, use of appropriate technology and usability issues. Poor usability leads to the distraction of users. Literature covers many aspects of usability evaluation of LMS. However, there is less focus on navigational issues. Poor navigational can lead to disorientation and cognitive overload of the users of any Web application. For this reason, we have proposed a navigational evaluation framework to evaluate the navigational structure of the LMS. We have applied this framework to evaluate the navigational structure of Moodle. We conducted a survey among students and teachers of two leading universities in Pakistan, where Moodle is in use. This work summarizes the survey results and proposes guidelines to improve the usability of Moodle based on the feedback received from its users.

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