Patient privacy protection using anonymous access control techniques

Objective: The objective of this study is to develop a solution to preserve security and privacy in a healthcare environment where health-sensitive information will be accessed by many parties and stored in various distributed databases. The solution should maintain anonymous medical records and it should be able to link anonymous medical information in distributed databases into a single patient medical record with the patient identity. Methods: In this paper we present a protocol that can be used to authenticate and authorize patients to healthcare services without providing the patient identification. Healthcare service can identify the patient using separate temporary identities in each identification session and medical records are linked to these temporary identities. Temporary identities can be used to enable record linkage and reverse track real patient identity in critical medical situations. Results: The proposed protocol provides main security and privacy services such as user anonymity, message privacy, message confidentiality, user authentication, user authorization and message replay attacks. The medical environment validates the patient at the healthcare service as a real and registered patient for the medical services. Using the proposed protocol, the patient anonymous medical records at different healthcare services can be linked into one single report and it is possible to securely reverse track anonymous patient into the real identity. Conclusion: The protocol protects the patient privacy with a secure anonymous authentication to healthcare services and medical record registries according to the European and the UK legislations, where the patient real identity is not disclosed with the distributed patient medical records

Enabling Collaborative eHealth Research using Web 2.0 Tools

In this paper, we describe two Web 2.0 based systems designed to facilitate and enhance collaborative eHealth research activities. Using a combination of Forums, Wikis and connectivity to 3rd party social networking systems, we have designed systems to support collaborative document creation (including editing, reviewing and publication), dissemination of material to relevant communities, discussion of ideas, and sharing of opinions. The ECDC Field Epidemiology Manual Wiki and Medicine Support Unit Online Forums are presented herein, including an overview to the system architectures, and user interaction models. We present our planned methods of evaluation, focusing on the ability to measure successful and sustainable community involvement

Modelling and Forecasting the Unit Cost of Electricity Generated by Fossil Fuel Power Plants in Sri Lanka

The national grid system which is evolved to deliver electricity must be always kept in balance so that it must have a sufficient production to meet the demand of electricity while minimizing the generation cost. This study presents a statistical time series model for forecasting the Unit Cost (UC) of generation of electricity in fossil fuel power plants by using two approaches namely Auto Regressive Integrated Moving Average (ARIMA) and time series regression. This is conducted as a case study in a Diesel/Heavy Fuel Oil (HFO) power plant in Sri Lanka which consists of two sub stations. ARIMA (1,1,0) and ARIMA (2,1,2) were selected as the best models with the lowest Akaike Information Criterion (AIC) under the ARIMA model approach while two dynamic regression models with coefficient of determination (R2) value 0.55 were selected under time series regression approach for Station 1 and Station 2 respectively. The regression model was identified as the best forecasting method for two stations with the minimum Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). The forecasts of the future generation cost of electricity are extensively helpful for the national grid system for financial and capacity planning, fuel management and operational planning

Supporting Constructive Video-based Learning: Requirements Elicitation from Exploratory Studies

Although videos are a highly popular digital medium for learning, video watching can be a passive activity and results in limited learning. This calls for interactive means to support engagement and active video watching. However, there is limited insight into what engagement challenges have to be overcome and what intelligent features are needed. This paper presents an empirical way to elicit requirements for innovative functionality to support constructive video-based learning. We present two user studies with an active video watching system instantiated for soft skill learning (pitch presenta-tions). Based on the studies, we identify whether learning is happening and what kind of interaction contributes to learning, what difficulties participants face and how these can be overcome with additional intelligent support. Our findings show that participants who engaged in constructive learning have improved their conceptual understanding of presentation skills, while those who exhibited more passive ways of learning have not improved as much as constructive learners. Analysis of participants’ profiles and experiences led to requirements for intelligent support with active video watching. Based on this, we propose intelligent nudging in the form of signposting and prompts to further promote constructive learning

Merkel cell carcinoma masquerading as cellulitis: A case report and review of the literature

© 2018 Multimed Inc. Merkel cell carcinoma (MCC) is an uncommon malignancy of the skin arising from cells located in the deeper layers of the epidermis called Merkel cells. This malignancy rarely presents as a metastatic disease, and the field is therefore deficient in regards to management. We report the case of a 49-year-old woman who presented with a presumptive diagnosis of osteomyelitis of the left fifth digit that was resistant to treatment with antibiotics; she underwent debridement of the digit that revealed MCC and was later to have metastatic disease to her lungs, liver, and musculoskeletal system. The management of MCC, although simple in the early stage of the disease, can become challenging when it is more advanced. Multiple new modalities for its treatment have emerged over the last few years, and more recently, clinical trials are being conducted for the use of immunotherapy agents in the treatment of this malignancy

ABC-based Forecasting in State Space Models

Approximate Bayesian Computation (ABC) has gained popularity as a method for conducting inference and forecasting in complex models, most notably those which are intractable in some sense. In this paper we use ABC to produce probabilistic forecasts in state space models (SSMs). Whilst ABC-based forecasting in correctly-specified SSMs has been studied, the misspecified case has not been investigated, and it is that case which we emphasize. We invoke recent principles of 'focused' Bayesian prediction, whereby Bayesian updates are driven by a scoring rule that rewards predictive accuracy; the aim being to produce predictives that perform well in that rule, despite misspecification. Two methods are investigated for producing the focused predictions. In a simulation setting, 'coherent' predictions are in evidence for both methods: the predictive constructed via the use of a particular scoring rule predicts best according to that rule. Importantly, both focused methods typically produce more accurate forecasts than an exact, but misspecified, predictive. An empirical application to a truly intractable SSM completes the paper