Ensemble Model for the Risk of Anemia in Pediatric Patients With Sickle Cell Disorder

Anemia is a major cause of morbidity and mortality of SCD patients in many parts of the world with the burden much higher in Sub Saharan Africa. This study developed an ensemble of machine learning algorithm for the prediction of the risk of anemia in pediatric SCD patients. Data for this study was collected from 115 pediatric SCD outpatients receiving treatment at a tertiary hospital in South-Western Nigeria. This study adopted a stack-ensemble model composed of deep neural network (DNN), multi-layer perceptron (MLP), and support vector machines (SVM) as base and meta-classifiers using the WEKA software. The ensemble models were compared following the stack-ensemble developed using SVM as a meta-classifier had the best performance with an accuracy of 72.7%. The study concluded that information about socio-demographic and clinical data can be used to assess the risk of anemia among SCD patients.

Patient Preparation and Positioning on Robot-Assisted and Robotic Surgeries

The field of robotic surgery is being developed and is increasingly being applied to many and different surgical procedures. This results in the need of looking for different patient placements, which will give the surgeon the best view of the area of clinical interest. The purpose of this paper is to study and investigate the various ways of patient preparation and positioning in the field of robotic surgery. Also, this paper proposes a classification and comparing between these different positions, depending on the procedure.

Applying Belief Rule-Based Inference Methodology Using Evidential Reasoning Approach to Clinical Reporting System

In the healthcare field, a critical issue is how to reason and represent uncertainties that present in clinical knowledge domain, signs, and symptoms to make the correct decisions. Although several researchers have developed various models of clinical modeling, many of them are incapable of handling uncertainties correctly. The paper provides the working details of rule-based inference methodology using evidence reasoning (RIMER) methodology applied to model the inference process and clinical guidelines. In RIMER, belief-degree are embedded in all possible consequences of the rule. It can handle uncertainties and provide a causal relationship between the rules. Traditional IF-THEN rules do not provide a causal relationship between antecedent and consequent attributes of the rule only provide that the rule is either 100% true or 100% false. Also, a case study is used to demonstrate that the results generated by the system using RIMER are more reliable in terms of accuracy and performance compared to results generated manually for a diabetes diagnosis.

Transforming Health Information Management Through the Digital Pen and Paper (DPP) Technology

The aim of this article is the development and use of a smart accountability system (both systemic and individual) during the provision of health services, which will free up a large number of resources within a Health Organization. Compared to traditional manual recording, the new system of Digital Pen and Paper (DPP) achieves a significant reduction in the organization's operating cost. Over the duration of seven months from January to July 2016, 693 forms were collected from three public hospitals of Greece. 49 nurses who worked in intensive care units (ICU) took part in the survey. 18,934 fields were filled in 1,466 of which were counted with errors. The accuracy percentage rate of the data amounts to 92.3% and the corresponding rate of errors to 7.7%. The average time for a form to be filled in by a nurse using the digital pen amounts to 3 minutes and 35 seconds. The highest percentage rate of data accuracy reached 94.76% and the best time of filling in the form was under 3 minutes per form. Results can be improved even more through user training and application of entry rules.

The CO.R.E. Project. An Integrated Security Approach to Self-Monitoring and Medical Record Keeping

The widespread use of electronic Personal Health Records is considered of great importance, however, until today, there is no widely adopted application paradigm for the functional specifications of a modern ePHR due to absence of trust, inadequate data completeness and overall use complexity and “unfriendliness”. CO.R.E. (COnsolidation & Routing Engine) is an innovative approach towards the development of a health data consolidation and cloud access provision infrastructure, taking under consideration both the needs for wide adoption and the application of mission critical technologies in real production environments. The CO.R.E. infrastructure provides an environment for deploying medical record applications with central storage and individually controlled distributed access, ensuring: a) the absence of readable identifiers in any network communication among the involved systems and b) the inability (as much as modern cryptographic methods offer) of anyone - even the engineers working on the system - to correlate the stored medical data with their owner/physical person.

Technological Solutions for Helping Adaptive Responding of Children with Severe to Profound Developmental Disabilities

This article provides the reader with a literature overview of the newest empirical evidences available on the use of assistive technology-based programs for teaching and improving adaptive responding of children with severe to profound developmental disabilities. Four main categories of studies were selected, regarding (a) the technology for promoting independent access to positive stimulation, (b) the combination between microswitch and VOCA aimed at asking for social contact with a caregiver, (c) the cluster technology for increasing an adaptive response, and simultaneously reducing a challenging behavior, (d) the technology for requesting and choosing desired item or the access to the literacy. Overall, 26 studies were reviewed, and 75 participants were involved. The outcomes were largely positive, although few failures occurred. Psychological and Rehabilitative implications of the findings for both research and clinical practices were critically discussed

Utilizing Information Technology in Senior Care Franchises

Franchising involves receiving business rights. In addition to the popular growth strategy for many businesses, franchising has emerged over the years as a pathway to wealth creation for entrepreneurs interested in senior care. In this article, the authors first discuss the use of information technology in senior care franchises, including franchisor/franchisee relationships and the essential indicators needed to allow a good relationship to flourish, and the inevitability of collaborative learning and innovation. This leads to the discussion of the working knowledge development among the franchisor and the fellow franchisees. Second, this paper discusses that an attention-based IT infrastructure that will enable the senior care knowledge sharing and dissemination between the franchisor and the franchisee.

A Biologically-Inspired Metaheuristic Approach for the Simultaneous Generation of Alternatives

Decision-making in the “real world” involves complex problems that tend to be riddled with competing performance objectives and possess requirements which are very difficult to incorporate into any underlying decision support models. There are invariably unmodelled elements, not apparent during model construction, which can greatly impact the acceptability of the model's solutions. Consequently, it is preferable to generate numerous dissimilar alternatives that provide disparate perspectives to the problem. These alternatives should possess near-optimal objective measures with respect to all known objectives, but be maximally different from each other in terms of their decision variables. This maximally different solution creation approach is referred to as modelling-to-generate-alternatives (MGA). This article provides an efficient biologically-inspired algorithm that simultaneously generates multiple, maximally different alternatives by employing the Firefly Algorithm metaheuristic. The effectiveness of this algorithm is demonstrated on an engineering optimization benchmark test problem

The Role of Artificial Intelligence in Clinical Decision Support Systems and a Classification Framework

Clinical decision support systems are meant to improve the quality of decision-making in healthcare. Artificial intelligence is the science of creating intelligent systems that solve complex problems at the level of or better than human experts. Combining artificial intelligence methods into clinical decision support will enable the utilization of large quantities of data to produce relevant decision-making information to practitioners. This article examines various artificial intelligence methodologies and shows how they may be incorporated into clinical decision-making systems. A framework for describing artificial intelligence applications in clinical decision support systems is presented.

Order Statistics and Clinical-Practice Studies

Statistics are essential tools in scientific studies and facilitate various hypothesis tests, such as test administration, response scoring, data analysis, and test interpretation. Order statistics refer to the collection of sample observations sorted in ascending order and are among the most fundamental tools in non-parametric statistics and inference. Statistical inference established based on order statistics assumes nothing stronger than continuity of the cumulative distribution function of the population and is simple and broadly applicable. The authors discuss how order statistics are applied in statistical analysis, e.g., tests of independence, tests of goodness of fit, hypothesis tests of equivalence of means, ranking and selection, and quantile estimation. These order-statistics techniques are key components of many clinical studies.