Simplifying HL7 Version 3 messages

Abstract. HL7 Version 3 offers a semantically robust method for healthcare interoperability but has been criticized as overly complex to implement. This paper reviews initiatives to simplify HL7 Version 3 messaging and presents a novel approach based on semantic mapping. Based on user-defined definitions, precise transforms between simple and full messages are automatically generated. Systems can be interfaced with the simple messages and achieve interoperability with full Version 3 messages through the transforms. This reduces the costs of HL7 interfacing and will encourage better uptake of HL7 Version 3 and CDA

Short-term leprosy forecasting from an expert opinion survey.

We conducted an expert survey of leprosy (Hansen's Disease) and neglected tropical disease experts in February 2016. Experts were asked to forecast the next year of reported cases for the world, for the top three countries, and for selected states and territories of India. A total of 103 respondents answered at least one forecasting question. We elicited lower and upper confidence bounds. Comparing these results to regression and exponential smoothing, we found no evidence that any forecasting method outperformed the others. We found evidence that experts who believed it was more likely to achieve global interruption of transmission goals and disability reduction goals had higher error scores for India and Indonesia, but lower for Brazil. Even for a disease whose epidemiology changes on a slow time scale, forecasting exercises such as we conducted are simple and practical. We believe they can be used on a routine basis in public health

Sensitivity analysis of an Advanced Gas-cooled Reactor control rod model

A model has been made of the primary shutdown system of an Advanced Gas-cooled Reactor nuclear power station. The aim of this paper is to explore the use of sensitivity analysis techniques on this model. The two motivations for performing sensitivity analysis are to quantify how much individual uncertain parameters are responsible for the model output uncertainty, and to make predictions about what could happen if one or several parameters were to change. Global sensitivity analysis techniques were used based on Gaussian process emulation; the software package GEM-SA was used to calculate the main effects, the main effect index and the total sensitivity index for each parameter and these were compared to local sensitivity analysis results. The results suggest that the system performance is resistant to adverse changes in several parameters at once

Police Criminal Charging Decisions: An Examination of Post-Arrest Decision-Making

Scholars have encouraged studies of police decision-making to move beyond the arrest decision into research that broadens the understanding of police behavior. The criminal charge placed by officers against offenders is largely an untouched area of study. Examining criminal charging decisions goes beyond simple dichotomous decisions, such as arrest, but instead explores the area of police leniency or punitiveness. Randomly constructed vignettes describing a domestic violence incident were given to officers from four agencies. Officers indicated the criminal charges they would likely list against an offender if they were to make an arrest. Serious criminal charges were often supported by additional, but less serious, charges. Victim injury and an uncooperative offender were related to the decision to charge a misdemeanor offense. There was a significant negative relationship between the number of charges listed and more experienced officers and officers working in smaller agencies. The implications of this study and directions for future research are discussed

Developing a conformance methodology for clinically-defined medical record headings:a preliminary report.

Background: The Professional Records Standards Body for health and social care (PRSB) was formed in 2013 to develop and assure professional standards for the content and structure of patient records across all care disciplines in the UK. Although the PRSB work is aimed at Electronic Health Record (EHR) adoption and interoperability to support continuity of care, the current technical guidance is limited and ambiguous. Objectives: This project was initiated as a proof-ofconcept to demonstrate whether, and if so, how, conformance methods can be developed based on the professional standards. Methods: An expert group was convened, comprising clinical and technical representatives. A constrained data set was defined for an outpatient letter, using the subset of outpatient headings that are also present in the ep-SOS patient summary. A mind map was produced for the main sections and sub-sections. An openEHR archetype model was produced as the basis for creating HL7 and IHE implementation artefacts. Results: Several issues about data definition and representation were identified when attempting to map the outpatient headings to the epSOS patient summary, partly due to the difference between process and static viewpoints. Mind maps have been a simple and helpful way to visualize the logical information model and expose and resolve disagreements about which headings are purely for human navigation and which, if any, have intrinsic meaning. Conclusions: Conformance testing is feasible but nontrivial. In contrast to traditional standards-development timescales, PRSB needs an agile standards development process with EHR vendor and integrator collaboration to ensure implementability and widespread adoption. This will require significant clinical and technical resources

Genomic islands 1 and 2 play key roles in the evolution of extensively drug-resistant ST235 isolates of Pseudomonas aeruginosa

© 2016 The Authors. Pseudomonas aeruginosa are noscomially acquired, opportunistic pathogens that pose a major threat to the health of burns patients and the immunocompromised. We sequenced the genomes of P. aeruginosa isolates RNS-PA1, RNS-PA46 and RNS-PAE05, which displayed resistance to almost all frontline antibiotics, including gentamicin, piperacillin, timentin, meropenem, ceftazidime and Colistin. We provide evidence that the isolates are representatives of P. aeruginosa sequence type (ST) 235 and carry Tn6162 and Tn6163 in genomic islands 1 (GI1) and 2 (GI2), respectively. Gil disrupts the endA gene at precisely the same chromosomal location as in P. aeruginosa strain VR-143/97, of unknown ST, creating an identical CA direct repeat. The class 1 integron associated with Tn6163 in GI2 carries a blaGES-5-aacA4-gcuE15-aphA15 cassette array conferring resistance to carbapenems and aminoglycosides. GI2 is flanked by a 12 nt direct repeat motif, abuts a tRNA-gly gene, and encodes proteins with putative roles in integration, conjugative transfer as well as integrative conjugative element-specific proteins. This suggests that GI2 may have evolved from a novel integrative conjugative element. Our data provide further support to the hypothesis that genomic islands play an important role in de novo evolution of multiple antibiotic resistance phenotypes in P. aeruginosa

Neck spasm after chemoradiotherapy for head and neck cancer: Natural history and dosimetric correlates

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/102727/1/hed23284.pd

Machine learning for energy load forecasting

With an increasing penetration of renewables into energy markets, it is desirable to have a flexible grid in order to match large fluctuations in supply to a volatile power output typical of renewable supply. Hence, it is imperative to accurately forecast power load demand. The recent emergence of big data analytics and machine learning techniques have shown great success in a wide range of regression problems in varied industries and various data can be harnessed by the energy industry to better understand likely energy loads placed upon the system. This paper presents a comparison of several regression models which can be used for accurate predictions of energy load given environmental feature data. Here we show that dynamic Gaussian Processes can be used as a powerful tool taking into account the non-stationarity of the data under analysis. This regression model was compared Neural Networks, used most extensively in the industry, and linear regression models to give an idea of their comparable accuracy. However, it was noted that the dynamic Gaussian Process were inferior to a Neural Network when training for huge datasets due to their high relative computational cost, increased uncertainty with projection time, and large memory usage. Though primarily used for dynamics problems, there are a range of non-stationary problems which could benefit from the use of a dynamic Gaussian Process of which this paper just presents one. It also considers online learning models be used for real time forecasting