An Advanced Conceptual Diagnostic Healthcare Framework for Diabetes and Cardiovascular Disorders

The data mining along with emerging computing techniques have astonishingly influenced the healthcare industry. Researchers have used different Data Mining and Internet of Things (IoT) for enrooting a programmed solution for diabetes and heart patients. However, still, more advanced and united solution is needed that can offer a therapeutic opinion to individual diabetic and cardio patients. Therefore, here, a smart data mining and IoT (SMDIoT) based advanced healthcare system for proficient diabetes and cardiovascular diseases have been proposed. The hybridization of data mining and IoT with other emerging computing techniques is supposed to give an effective and economical solution to diabetes and cardio patients. SMDIoT hybridized the ideas of data mining, Internet of Things, chatbots, contextual entity search (CES), bio-sensors, semantic analysis and granular computing (GC). The bio-sensors of the proposed system assist in getting the current and precise status of the concerned patients so that in case of an emergency, the needful medical assistance can be provided. The novelty lies in the hybrid framework and the adequate support of chatbots, granular computing, context entity search and semantic analysis. The practical implementation of this system is very challenging and costly. However, it appears to be more operative and economical solution for diabetes and cardio patients.Comment: 11 PAGE

Global and Episodic Bundling: An Overview and Considerations for Medicaid

Examines implementation issues for two payment strategies under which a group of providers receives a single payment per patient for a predefined time period for a predefined set of services and which involve risk adjustment and quality measurement

Can high-frequency ultrasound predict metastatic lymph nodes in patients with invasive breast cancer?

Aim To determine whether high-frequency ultrasound can predict the presence of metastatic axillary lymph nodes, with a high specificity and positive predictive value, in patients with invasive breast cancer. The clinical aim is to identify patients with axillary disease requiring surgery who would not normally, on clinical grounds, have an axillary dissection, so potentially improving outcome and survival rates. Materials and methods The ipsilateral and contralateral axillae of 42 consecutive patients with invasive breast cancer were scanned prior to treatment using a B-mode frequency of 13 MHz and a Power Doppler frequency of 7 MHz. The presence or absence of an echogenic centre for each lymph node detected was recorded, and measurements were also taken to determine the L/S ratio and the widest and narrowest part of the cortex. Power Doppler was also used to determine vascularity. The contralateral axilla was used as a control for each patient. Results In this study of patients with invasive breast cancer, ipsilateral lymph nodes with a cortical bulge ≥3 mm and/or at least two lymph nodes with absent echogenic centres indicated the presence of metastatic axillary lymph nodes (10 patients). The sensitivity and specificity were 52.6% and 100%, respectively, positive and negative predictive values were 100% and 71.9%, respectively, the P value was 0.001 and the Kappa score was 0.55.\ud Conclusion This would indicate that high-frequency ultrasound can be used to accurately predict metastatic lymph nodes in a proportion of patients with invasive breast cancer, which may alter patient management

Semantic processing of EHR data for clinical research

There is a growing need to semantically process and integrate clinical data from different sources for clinical research. This paper presents an approach to integrate EHRs from heterogeneous resources and generate integrated data in different data formats or semantics to support various clinical research applications. The proposed approach builds semantic data virtualization layers on top of data sources, which generate data in the requested semantics or formats on demand. This approach avoids upfront dumping to and synchronizing of the data with various representations. Data from different EHR systems are first mapped to RDF data with source semantics, and then converted to representations with harmonized domain semantics where domain ontologies and terminologies are used to improve reusability. It is also possible to further convert data to application semantics and store the converted results in clinical research databases, e.g. i2b2, OMOP, to support different clinical research settings. Semantic conversions between different representations are explicitly expressed using N3 rules and executed by an N3 Reasoner (EYE), which can also generate proofs of the conversion processes. The solution presented in this paper has been applied to real-world applications that process large scale EHR data.Comment: Accepted for publication in Journal of Biomedical Informatics, 2015, preprint versio

Failure mode identification and end of life scenarios of offshore wind turbines: a review

In 2007, the EU established challenging goals for all Member States with the aim of obtaining 20% of their energy consumption from renewables, and offshore wind is expected to be among the renewable energy sources contributing highly towards achieving this target. Currently wind turbines are designed for a 25-year service life with the possibility of operational extension. Extending their efficient operation and increasing the overall electricity production will significantly increase the return on investment (ROI) and decrease the levelized cost of electricity (LCOE), considering that Capital Expenditure (CAPEX) will be distributed over a larger production output. The aim of this paper is to perform a detailed failure mode identification throughout the service life of offshore wind turbines and review the three most relevant end of life (EOL) scenarios: life extension, repowering and decommissioning. Life extension is considered the most desirable EOL scenario due to its profitability. It is believed that combining good inspection, operations and maintenance (O&M) strategies with the most up to date structural health monitoring and condition monitoring systems for detecting previously identified failure modes, will make life extension feasible. Nevertheless, for the cases where it is not feasible, other options such as repowering or decommissioning must be explored