The Assessment of Patient Clinical Outcome: Advantages, Models, Features of an Ideal Model

Background: The assessment of patient clinical outcome focuses on measuring various aspects of the health status of a patient who is under healthcare intervention. Patient clinical outcome assessment is a very significant process in the clinical field as it allows health care professionals to better understand the effectiveness of their health care programs and thus for enhancing the health care quality in general. It is thus vital that a high quality, informative review of current issues regarding the assessment of patient clinical outcome should be conducted. Aims & Objectives: 1) Summarizes the advantages of the assessment of patient clinical outcome; 2) reviews some of the existing patient clinical outcome assessment models namely: Simulation, Markov, Bayesian belief networks, Bayesian statistics and Conventional statistics, and Kaplan-Meier analysis models; and 3) demonstrates the desired features that should be fulfilled by a well-established ideal patient clinical outcome assessment model. Material & Methods: An integrative review of the literature has been performed using the Google Scholar to explore the field of patient clinical outcome assessment. Conclusion: This paper will directly support researchers, clinicians and health care professionals in their understanding of developments in the domain of the assessment of patient clinical outcome, thus enabling them to propose ideal assessment models

A doctor recommender system based on collaborative and content filtering

The volume of healthcare information available on the internet has exploded in recent years. Nowadays, many online healthcare platforms provide patients with detailed information about doctors. However, one of the most important challenges of such platforms is the lack of personalized services for supporting patients in selecting the best-suited doctors. In particular, it becomes extremely time-consuming and difficult for patients to search through all the available doctors. Recommender systems provide a solution to this problem by helping patients gain access to accommodating personalized services, specifically, finding doctors who match their preferences and needs. This paper proposes a hybrid content-based multi-criteria collaborative filtering approach for helping patients find the best-suited doctors who meet their preferences accurately. The proposed approach exploits multi-criteria decision making, doctor reputation score, and content information of doctors in order to increase the quality of recommendations and reduce the influence of data sparsity. The experimental results based on a real-world healthcare multi-criteria (MC) rating dataset show that the proposed approach works effectively with regard to predictive accuracy and coverage under extreme levels of sparsity

Restaurant Recommendations Based on Multi-Criteria Recommendation Algorithm

Recent years have witnessed a rapid explosion of online information sources about restaurants, and the selection of an appropriate restaurant has become a tedious and time-consuming task. A number of online platforms allow users to share their experiences by rating restaurants based on more than one criterion, such as food, service, and value. For online users who do not have enough information about suitable restaurants, ratings can be decisive factors when choosing a restaurant. Thus, personalized systems such as recommender systems are needed to infer the preferences of each user and then satisfy those preferences. Specifically, multi-criteria recommender systems can utilize the multi-criteria ratings of users to learn their preferences and suggest the most suitable restaurants for them to explore. Accordingly, this paper proposes an effective multi-criteria recommender algorithm for personalized restaurant recommendations. The proposed Hybrid User-Item based Multi-Criteria Collaborative Filtering algorithm exploits users’ and items’ implicit similarities to eliminate the sparseness of rating information. The experimental results based on three real-word datasets demonstrated the validity of the proposed algorithm concerning prediction accuracy, ranking performance, and prediction coverage, specifically, when dealing with extremely sparse datasets, in relation to other baseline CF-based recommendation algorithms.

Secure Cloud-Mediator Architecture for Mobile-Government using RBAC and DUKPT

Smart mobile devices and cloud computing are widely used today. While mobile and portable devices have different capabilities, architectures, operating systems, and communication channels than one another, government data are distributed over heterogeneous systems. This paper proposes a 3-tier mediation framework providing single application to manage all governmental services. The framework is based on private cloud computing for adapting the content of Mobile-Government (M-Government) services using Role-Based Access Control (RBAC) and Derive Unique Key Per Transaction (DUKPT). The 3-layers in the framework are: presence, integration, and homogenization. The presence layer is responsible for adapting the content with regard to four contexts: device, personal, location, and connectivity contexts. The integration layer, which is hosted in a private cloud server, is responsible for integrating heterogeneous data sources. The homogenization layer is responsible for converting data into XML format. The flexibility of the mediation and XML provides an adaptive environment to stream data based on the capabilities of the device that sends the query to the system.</p

A Trust-Based Recommender System for Personalized Restaurants Recommendation

Several online restaurant applications, such as TripAdvisor and Yelp, provide potential consumers with reviews and ratings based on previous customers’ experiences. These reviews and ratings are considered the most important factors that determine the customer’s choice of restaurants. However, the selection of a restaurant among many unknown choices is still an arduous and time- consuming task, particularly for tourists and travellers. Recommender systems utilize the ratings provided by users to assist them in selecting the best option from many options based on their preferences. In this paper, we propose a trust-based recommendation model for helping consumers select suitable restaurants in accordance with their preferences. The proposed model utilizes multi- criteria ratings of restaurants and implicit trust relationships among consumers to produce personalized restaurant recommendations. The experimental results based on a real-world restaurant dataset demonstrated the superiority of the proposed model, in terms of prediction accuracy and coverage, in overcoming the sparsity and new user problems when compared to other baseline CF-based recommendation algorithms

ARABIC TEXT CATEGORIZATION ALGORITHM USING VECTOR EVALUATION METHOD

ABSTRACT Text categorization is the process of grouping documents into categories based on their contents. This process is important to make information retrieval easier, and it became more important due to the huge textual information available online. The main problem in text categorization is how to improve the classification accuracy. Although Arabic text categorization is a new promising field, there are a few researches in this field. This paper proposes a new method for Arabic text categorization using vector evaluation. The proposed method uses a categorized Arabic documents corpus , and then the weights of the tested document&apos;s words are calculated to determine the document keywords which will be compared with the keywords of the corpus categorizes to determine the tested document&apos;s best category