Background: Patient monitoring is vital in all stages of care. In particular, intensive care unit (ICU) patient monitoring has the potential to reduce complications and morbidity, and to increase the quality of care by enabling hospitals to deliver higher-quality, cost-effective patient care, and improve the quality of medical services in the ICU. Objective: We here report the development and validation of ICU length of stay and mortality prediction models. The models will be used in an intelligent ICU patient monitoring module of an Intelligent Remote Patient Monitoring (IRPM) framework that monitors the health status of patients, and generates timely alerts, maneuver guidance, or reports when adverse medical conditions are predicted. Methods: We utilized the publicly available Medical Information Mart for Intensive Care (MIMIC) database to extract ICU stay data for adult patients to build two prediction models: one for mortality prediction and another for ICU length of stay. For the mortality model, we applied six commonly used machine learning (ML) binary classification algorithms for predicting the discharge status (survived or not). For the length of stay model, we applied the same six ML algorithms for binary classification using the median patient population ICU stay of 2.64 days. For the regression-based classification, we used two ML algorithms for predicting the number of days. We built two variations of each prediction model: one using 12 baseline demographic and vital sign features, and the other based on our proposed quantiles approach, in which we use 21 extra features engineered from the baseline vital sign features, including their modified means, standard deviations, and quantile percentages. Results: We could perform predictive modeling with minimal features while maintaining reasonable performance using the quantiles approach. The best accuracy achieved in the mortality model was approximately 89% using the random forest algorithm. The highest accuracy achieved in the length of stay model, based on the population median ICU stay (2.64 days), was approximately 65% using the random forest algorithm. Conclusions: The novelty in our approach is that we built models to predict ICU length of stay and mortality with reasonable accuracy based on a combination of ML and the quantiles approach that utilizes only vital signs available from the patient’s profile without the need to use any external features. This approach is based on feature engineering of the vital signs by including their modified means, standard deviations, and quantile percentages of the original features, which provided a richer dataset to achieve better predictive power in our models.

Purpose: This paper aims to provide a number of distinct approaches towards this goal, i.e. to translate the information contained in the repositories into knowledge. For centuries, humans have gathered and generated data to study the different phenomena around them. Consequently, there are a variety of information repositories available in many different fields of study. However, the ability to access, integrate and properly interpret the relevant data sets in these repositories has mainly been limited by their ever expanding volumes. The goal of translating the available data to knowledge, eventually leading to wisdom, requires an understanding of the relations, ordering and associations among the data sets.Design/methodology/approach: While the existing information repositories are rich in content, there are no easy means of understanding the relevance or influence of the different facts contained therein. Therefore, the interest of the general populace in terms of prioritizing some data items (or facts) over others is usually lost. In this paper, the goal is to provide approaches for transforming the available facts in the information repositories to wisdom. The authors target the lack of order in the facts presented in the repositories to create a hierarchical distribution based on the common understanding, expectations, opinions and judgments of the different users.Findings: The authors present multiple approaches to extract and order the facts related to each concept, using both automatic and semi-automatic methods. The experiments show that the results of these approaches are similar and very close to the instinctive ordering of facts by users.Originality/value: The authors believe that the work presented in this paper, with some additions, can be a feasible step to convert the available knowledge to wisdom and a step towards the future of online information systems.

The proliferation of private clouds that are often underutilized and the tremendous computational potential of these clouds when combined has recently brought forth the idea of volunteer cloud computing (VCC), a computing model where cloud owners contribute underutilized computing and/or storage resources on their clouds to support the execution of applications of other members in the community. This model is particularly suitable to solve big data scientific problems. Scientists in data-intensive scientific fields increasingly recognize that sharing volunteered resources from several clouds is a cost-effective alternative to solve many complex, data- and/or compute-intensive science problems. Despite the promise of the idea of VCC, it still remains at the vision stage at best. Challenges include the heterogeneity and autonomy of member clouds, access control and security, complex inter-cloud virtual machine scheduling, etc. In this paper, we present CloudFinder, a system that supports the efficient execution of big data workloads on volunteered federated clouds (VFCs). Our evaluation of the system indicates that VFCs are a promising cost-effective approach to enable big data science.

Software as a service is a well accepted software deployment and distribution model that has grown exponentially in the last few years. One of the biggest benefits of SaaS is the automated composition of different services in a composite system. It allows users to automatically find and bind these services (to maximize the productivity), meeting both functional and non-functional requirements. In this paper, we present a framework for modeling the dependency relationships among different Quality of Service parameters of the component services. Our proposed approach considers the different invocation patterns of component services, and presents a service composition framework to model the dependencies and uses the global QoS for service selection. We evaluate the efficiency of our proposed technique on the WSDream-QoS Dataset [1] .