Hybrid model checking approach to analysing rule conformance applied to HIPAA privacy rules, A

2017 Summer.Includes bibliographical references.Many of today's computing systems must show evidence of conformance to rules. The rules may come from business protocol choices or from multi-jurisdictional sources. Some examples are the rules that come from the regulations in the Health Insurance Portability and Accountability Act (HIPAA) protecting the privacy of patient information and the Family Educational Rights and Privacy Act (FERPA) protecting the privacy of student education records. The rules impose additional requirements on already complex systems, and rigorous analysis is needed to show that any system implementing the rules exhibit conformance. If the analysis finds that a rule is not satisfied, we adjudge that the system fails conformance analysis and that it contains a fault, and this fault must be located in the system and fixed. The exhaustive analysis performed by Model Checking makes it suitable for showing that systems satisfy conformance rules. Conformance rules may be viewed in two, sometimes overlapping, categories: process- aware conformance rules that dictate process sequencing, and data-aware conformance rules that dictate acceptable system states. Where conformance rules relate to privacy, the analysis performed in model check- ing requires the examination of fine-grained structural details in the system state for showing conformance to data-aware conformance rules. The analysis of these rules may cause model checking to be intractable due to a state space explosion when there are too many system states or too many details in a system state. To over- come this intractable complexity, various abstraction techniques have been proposed that achieve a smaller abstracted system state model that is more amenable to model checking. These abstraction techniques are not useful when the abstractions hide the details necessary to verify conformance. If non-conformance occurs, the abstraction may not allow isolation of the fault. In this dissertation, we introduce a Hybrid Model Checking Approach (HMCA) to analyse a system for both process- and data-aware conformance rules without abstracting the details from a system's detailed process- and data models. Model Checking requires an analysable model of the system under analysis called a program graph and a representation of the rules that can be checked on the program graph. In our approach, we use connections between a process-oriented (e.g. a Unified Modelling Language (UML) activity model) and a data-oriented (e.g. UML class model) to create a unified paths-and-state system model. We represent this unified model as a UML state machine. The rule-relevant part of the state machine along with a graph-oriented formalism of the rules are the inputs to HMCA. The model checker uses an exhaustive unfolding of the program graph to produce a transition system showing all the program graph's reachable paths and states. Intractable complexity during model checking is encountered when trying to create the transition system. In HMCA, we use a divide and conquer approach that applies a slicing technique on the program graph to semi- automatically produce the transition system by analysing each slice individually, and composing its result with the results from other slices. Our ability to construct the transition system from the slices relieves a traditional model checker of that step. We then return to use model checking techniques to verify whether the transition system satisfies the rules. Since the analysis involves examining system states, if any of the rules are not satisfied, we can isolate the specific location of the fault from the details contained in the slices. We demonstrate our technique on an instance of a medical research system whose requirements include the privacy rules mandated by HIPAA. Our technique found seeded faults for common mistakes in logic that led to non-conformance and underspecification leading to conflicts of interests in personnel relationships

An investigation of Reablement or restorative homecare interventions and outcome effects: A systematic review of randomised control trials

The effect of Reablement, a multi-faceted intervention is unclear, specifically, which interventions improve outcomes. This Systematic Review evaluates randomised controlled trials (RCTs) describing Reablement investigating the population, interventions, who delivered them, the effect and sustainability of outcomes. Database search from inception to August 2021 included AMED, ASSIA, BNI, CINHALL, EMBASE, HMIC, MEDLINE, PUBMED, PsycINFO, Google Scholar, Web of Science, Clinicaltrials.gov. Two researchers undertook data collection and quality assessment, following the PRISMA (2020) statement. They measured effect by changed primary or secondary outcomes: no ongoing service, functional ability, quality of life and mobility. The reviewers reported the analysis narratively, due to heterogeneity of outcome measures, strengthened by the SWiM reporting guideline. The search criteria resulted in eight international studies, five studies had a risk of bias limitations in either design or method. Ongoing service requirement decreased in five studies, with improved effect at 3 months shown in studies with occupational therapist involvement. Functional ability increased statistically in four studies at 3 months. Increase in quality of life was statistically significant in three studies, at 6 and 7 months. None of the studies reported a statistically significant improvement in functional mobility. Reablement is effective in the context of Health and Social Care. The outcomes were sustained at 3 months, with less sustainability at 6 months. There was no statistical result for the professional role regarding assessment, delivery and evaluation of interventions, and further research is justified

Retrieval of the Complete Coding Sequence of the UK-Endemic Tatenale Orthohantavirus Reveals Extensive Strain Variation and Supports Its Classification as a Novel Species

©2020 by the authors. Licensee MDPI, Basel, Switzerland. Orthohantaviruses are globally distributed viruses, associated with rodents and other small mammals. However, data on the circulation of orthohantaviruses within the UK, particularly the UK-endemic Tatenale virus, is sparse. In this study, 531 animals from five rodent species were collected from two locations in northern and central England and screened using a degenerate, pan- orthohantavirus RT-PCR assay. Tatenale virus was detected in a single field vole (Microtus agrestis) from central England and twelve field voles from northern England. Unbiased high-throughput sequencing of the central English strain resulted in the recovery of the complete coding sequence of a novel strain of Tatenale virus, whilst PCR-primer walking of the northern English strain recovered almost complete coding sequence of a previously identified strain. These findings represented the detection of a third lineage of Tatenale virus in the United Kingdom and extended the known geographic distribution of these viruses from northern to central England. Furthermore, the recovery of the complete coding sequence revealed that Tatenale virus was sufficiently related to the recently identified Traemersee virus, to meet the accepted criteria for classification as a single species of orthohantavirus

Eating disorder features in indigenous Aboriginal and Torres Strait Islander Australian Peoples

<p>Abstract</p> <p>Background</p> <p>Obesity and related cardiovascular and metabolic conditions are well recognized problems for Australian Aboriginal and Torres Strait Islander peoples. However, there is a dearth of research on relevant eating disorders (EDs) such as binge eating disorder in these groups.</p> <p>Methods</p> <p>Data were obtained from interviews of 3047 (in 2005) and 3034 (in 2008) adults who were participants in a randomly selected South Australian household survey of individuals' age > 15 years. The interviewed comprised a general health survey in which ED questions were embedded. Data were weighted according to national census results and comprised key features of ED symptoms.</p> <p>Results</p> <p>In 2005 there were 94 (85 weighted) First Australian respondents, and in 2008 65 (70 weighted). Controlling for secular differences, in 2005 rates of objective binge eating and levels of weight and shape influence on self-evaluation were significantly higher in indigenous compared to non-indigenous participants, but no significant differences were found in ED features in 2008.</p> <p>Conclusions</p> <p>Whilst results on small numbers must be interpreted with caution, the main finding was consistent over the two samples. For First Australians ED symptoms are at least as frequent as for non-indigenous Australians.</p

Effect of remote ischaemic conditioning on clinical outcomes in patients with acute myocardial infarction (CONDI-2/ERIC-PPCI): a single-blind randomised controlled trial.

BACKGROUND: Remote ischaemic conditioning with transient ischaemia and reperfusion applied to the arm has been shown to reduce myocardial infarct size in patients with ST-elevation myocardial infarction (STEMI) undergoing primary percutaneous coronary intervention (PPCI). We investigated whether remote ischaemic conditioning could reduce the incidence of cardiac death and hospitalisation for heart failure at 12 months. METHODS: We did an international investigator-initiated, prospective, single-blind, randomised controlled trial (CONDI-2/ERIC-PPCI) at 33 centres across the UK, Denmark, Spain, and Serbia. Patients (age >18 years) with suspected STEMI and who were eligible for PPCI were randomly allocated (1:1, stratified by centre with a permuted block method) to receive standard treatment (including a sham simulated remote ischaemic conditioning intervention at UK sites only) or remote ischaemic conditioning treatment (intermittent ischaemia and reperfusion applied to the arm through four cycles of 5-min inflation and 5-min deflation of an automated cuff device) before PPCI. Investigators responsible for data collection and outcome assessment were masked to treatment allocation. The primary combined endpoint was cardiac death or hospitalisation for heart failure at 12 months in the intention-to-treat population. This trial is registered with ClinicalTrials.gov (NCT02342522) and is completed. FINDINGS: Between Nov 6, 2013, and March 31, 2018, 5401 patients were randomly allocated to either the control group (n=2701) or the remote ischaemic conditioning group (n=2700). After exclusion of patients upon hospital arrival or loss to follow-up, 2569 patients in the control group and 2546 in the intervention group were included in the intention-to-treat analysis. At 12 months post-PPCI, the Kaplan-Meier-estimated frequencies of cardiac death or hospitalisation for heart failure (the primary endpoint) were 220 (8·6%) patients in the control group and 239 (9·4%) in the remote ischaemic conditioning group (hazard ratio 1·10 [95% CI 0·91-1·32], p=0·32 for intervention versus control). No important unexpected adverse events or side effects of remote ischaemic conditioning were observed. INTERPRETATION: Remote ischaemic conditioning does not improve clinical outcomes (cardiac death or hospitalisation for heart failure) at 12 months in patients with STEMI undergoing PPCI. FUNDING: British Heart Foundation, University College London Hospitals/University College London Biomedical Research Centre, Danish Innovation Foundation, Novo Nordisk Foundation, TrygFonden

Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies

Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms. Amongst military personnel, the success rates of this surgery can be as low as 20%, presenting a challenge in determining whether surgery is worthwhile. In this study, the data of 132 fasciotomies for CECS was analysed and using combinatorial feature selection methods, coupled with input from clinicians, identified a set of key clinical features contributing to the occupational outcomes of surgery. Features were utilised to develop a machine learning model for predicting return-to-work outcomes 12-months post-surgery. An AUC of 0.85 ± 0.08 was achieved using a linear-SVM, trained using 6 features (height, mean arterial pressure, pre-surgical score on the exercise-induced leg pain questionnaire, time from initial presentation to surgery, and whether a patient had received a prior surgery for CECS). To facilitate trust and transparency, interrogation strategies were used to identify reasons why certain patients were misclassified, using instance hardness measures. Model interrogation revealed that patient difficulty was associated with an overlap in the clinical characteristics of surgical outcomes, which was best handled by XGBoost and SVM-based models. The methodology was compiled into a machine learning framework, termed AITIA, which can be applied to other clinical problems. AITIA extends the typical machine learning pipeline, integrating the proposed interrogation strategy, allowing to user to reason and decide whether to trust the developed model based on the sensibility of its decision-making

Supplementary information files for Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies

Chronic exertional compartment syndrome (CECS) is a condition occurring most frequently in the lower limbs and often requires corrective surgery to alleviate symptoms. Amongst military personnel, the success rates of this surgery can be as low as 20%, presenting a challenge in determining whether surgery is worthwhile. In this study, the data of 132 fasciotomies for CECS was analysed and using combinatorial feature selection methods, coupled with input from clinicians, identified a set of key clinical features contributing to the occupational outcomes of surgery. Features were utilised to develop a machine learning model for predicting return-to-work outcomes 12-months post-surgery. An AUC of 0.85 ± 0.08 was achieved using a linear-SVM, trained using 6 features (height, mean arterial pressure, pre-surgical score on the exercise-induced leg pain questionnaire, time from initial presentation to surgery, and whether a patient had received a prior surgery for CECS). To facilitate trust and transparency, interrogation strategies were used to identify reasons why certain patients were misclassified, using instance hardness measures. Supplementary files for Predicting surgical outcomes for chronic exertional compartment syndrome using a machine learning framework with embedded trust by interrogation strategies.Model interrogation revealed that patient difficulty was associated with an overlap in the clinical characteristics of surgical outcomes, which was best handled by XGBoost and SVM-based models. The methodology was compiled into a machine learning framework, termed AITIA, which can be applied to other clinical problems. AITIA extends the typical machine learning pipeline, integrating the proposed interrogation strategy, allowing to user to reason and decide whether to trust the developed model based on the sensibility of its decision-making.</div