Severe hypoglycaemia in type 1 diabetes mellitus: underlying drivers and potential strategies for successful prevention

Hypoglycaemia remains an over-riding factor limiting optimal glycaemic control in type 1 diabetes. Severe hypoglycaemia is prevalent in almost half of those with long-duration diabetes and is one of the most feared diabetes-related complications. In this review, we present an overview of the increasing body of literature seeking to elucidate the underlying pathophysiology of severe hypoglycaemia and the limited evidence behind the strategies employed to prevent episodes. Drivers of severe hypoglycaemia including impaired counter-regulation, hypoglycaemia-associated autonomic failure, psychosocial and behavioural factors and neuroimaging correlates are discussed. Treatment strategies encompassing structured education, insulin analogue regimens, continuous subcutaneous insulin infusion pumps, continuous glucose sensing and beta-cell replacement therapies have been employed, yet there is little randomized controlled trial evidence demonstrating effectiveness of new technologies in reducing severe hypoglycaemia. Optimally designed interventional trials evaluating these existing technologies and using modern methods of teaching patients flexible insulin use within structured education programmes with the specific goal of preventing severe hypoglycaemia are required. Individuals at high risk need to be monitored with meticulous collection of data on awareness, as well as frequency and severity of all hypoglycaemic episodes.

Structural classification of proteins through amino acid sequence using interval type-2 fuzzy logic system

This paper introduces a new multi-output interval type-2 fuzzy logic system (MOIT2FLS) that is automatically constructed from unsupervised data clustering method and trained using heuristic genetic algorithm for a protein secondary structure classification. Three structure classes are distinguished including helix, strand (sheet) and coil which correspond to three outputs of the MOIT2FLS. Quantitative properties of amino acids are used to characterize the twenty amino acids rather than the widely used computationally expensive binary encoding scheme. Amino acid sequences are parsed into learnable patterns using a local moving window strategy. Three clustering tasks are performed using the adaptive vector quantization method to derive an equal number of initial rules for each type of secondary structure. Genetic algorithm is applied to optimally adjust parameters of the MOIT2FLS with the purpose of maximizing the Q3 measure. Comprehensive experimental results demonstrate the strong superiority of the proposed approach over the traditional methods including Chou-Fasman method, Garnier-Osguthorpe-Robson method, and artificial neural network models.

What's missing in missing data? Omissions in survey responses among parents of children with advanced cancer

Background: Missing data is a common phenomenon with survey-based research; patterns of missing data may elucidate why participants decline to answer certain questions. Objective: To describe patterns of missing data in the Pediatric Quality of Life and Evaluation of Symptoms Technology (PediQUEST) study, and highlight challenges in asking sensitive research questions. Design: Cross-sectional, survey-based study embedded within a randomized controlled trial. Setting: Three large children's hospitals: Dana-Farber/Boston Children's Cancer and Blood Disorders Center (DF/BCCDC); Children's Hospital of Philadelphia (CHOP); and Seattle Children's Hospital (SCH). Measurements: At the time of their child's enrollment, parents completed the Survey about Caring for Children with Cancer (SCCC), including demographics, perceptions of prognosis, treatment goals, quality of life, and psychological distress. Results: Eighty-six of 104 parents completed surveys (83% response). The proportion of missing data varied by question type. While 14 parents (16%) left demographic fields blank, over half (n=48; 56%) declined to answer at least one question about their child's prognosis, especially life expectancy. The presence of missing data was unrelated to the child's diagnosis, time from progression, time to death, or parent distress (p>0.3 for each). Written explanations in survey margins suggested that addressing a child's life expectancy is particularly challenging for parents. Conclusions and Relevance: Parents of children with cancer commonly refrain from answering questions about their child's prognosis, however, they may be more likely to address general cure likelihood than explicit life expectancy. Understanding acceptability of sensitive questions in survey-based research will foster higher quality palliative care research. © Copyright 2014, Mary Ann Liebert, Inc. 2014.

An interval type-2 fuzzy logic system-based method for prediction interval construction

This paper introduces a new non-parametric method for uncertainty quantification through construction of prediction intervals (PIs). The method takes the left and right end points of the type-reduced set of an interval type-2 fuzzy logic system (IT2FLS) model as the lower and upper bounds of a PI. No assumption is made in regard to the data distribution, behaviour, and patterns when developing intervals. A training method is proposed to link the confidence level (CL) concept of PIs to the intervals generated by IT2FLS models. The new PI-based training algorithm not only ensures that PIs constructed using IT2FLS models satisfy the CL requirements, but also reduces widths of PIs and generates practically informative PIs. Proper adjustment of parameters of IT2FLSs is performed through the minimization of a PI-based objective function. A metaheuristic method is applied for minimization of the non-linear non-differentiable cost function. Performance of the proposed method is examined for seven synthetic and real world benchmark case studies with homogenous and heterogeneous noise. The demonstrated results indicate that the proposed method is capable of generating high quality PIs. Comparative studies also show that the performance of the proposed method is equal to or better than traditional neural network-based methods for construction of PIs in more than 90% of cases. The superiority is more evident for the case of data with a heterogeneous noise. © 2014 Elsevier B.V.

Improving data forwarding in mobile social networks with infrastructure support: a space-crossing community approach

In this paper, we study two tightly coupled issues: space-crossing community detection and its influence on data forwarding in Mobile Social Networks (MSNs) by taking the hybrid underlying networks with infrastructure support into consideration. The hybrid underlying network is composed of large numbers of mobile users and a small portion of Access Points (APs). Because APs can facilitate the communication among long-distance nodes, the concept of physical proximity community can be extended to be one across the geographical space. In this work, we first investigate a space-crossing community detection method for MSNs. Based on the detection results, we design a novel data forwarding algorithm SAAS (Social Attraction and AP Spreading), and show how to exploit the space-crossing communities to improve the data forwarding efficiency. We evaluate our SAAS algorithm on real-life data from MIT Reality Mining and University of Illinois Movement (UIM). Results show that space-crossing community plays a positive role in data forwarding in MSNs in terms of delivery ratio and delay. Based on this new type of community, SAAS achieves a better performance than existing social community-based data forwarding algorithms in practice, including Bubble Rap and Nguyen's Routing algorithms. © 2014 IEEE.

Self-monitoring of blood glucose versus self-monitoring of urine glucose in adults with newly diagnosed Type 2 diabetes receiving structured education: a cluster randomized controlled trial.

AIMS: To compare the effectiveness and acceptability of self-monitoring of blood glucose with self-monitoring of urine glucose in adults with newly diagnosed Type 2 diabetes. METHODS: We conducted a multi-site cluster randomized controlled trial with practice-level randomization. Participants attended a structured group education programme, which included a module on self-monitoring using blood glucose or urine glucose monitoring. HbA1c and other biomedical measures as well as psychosocial data were collected at 6, 12 and 18 months. A total of 292 participants with Type 2 diabetes were recruited from 75 practices. RESULTS: HbA1c levels were significantly lower at 18 months than at baseline in both the blood monitoring group [mean (se) -12 (2) mmol/mol; -1.1 (0.2) %] and the urine monitoring group [mean (se) -13 (2) mmol/mol; -1.2 (0.2)%], with no difference between groups [mean difference adjusted for cluster effect and baseline value = -1 mmol/mol (95% CI -3, 2); -0.1% (95% CI -0.3, 0.2)]. Similar improvements were observed for the other biomedical outcomes, with no differences between groups. Both groups showed improvements in total treatment satisfaction, generic well-being, and diabetes-specific well-being, and had a less threatening view of diabetes, with no differences between groups at 18 months. Approximately one in five participants in the urine monitoring arm switched to blood monitoring, while those in the blood monitoring arm rarely switched (18 vs 1% at 18 months; P < 0.001). CONCLUSIONS: Participants with newly diagnosed Type 2 diabetes who attended structured education showed similar improvements in HbA1c levels at 18 months, regardless of whether they were assigned to blood or urine self-monitoring.

EEG signal classification for BCI applications by wavelets and interval type-2 fuzzy logic systems

The nonlinear, noisy and outlier characteristics of electroencephalography (EEG) signals inspire the employment of fuzzy logic due to its power to handle uncertainty. This paper introduces an approach to classify motor imagery EEG signals using an interval type-2 fuzzy logic system (IT2FLS) in a combination with wavelet transformation. Wavelet coefficients are ranked based on the statistics of the receiver operating characteristic curve criterion. The most informative coefficients serve as inputs to the IT2FLS for the classification task. Two benchmark datasets, named Ia and Ib, downloaded from the brain-computer interface (BCI) competition II, are employed for the experiments. Classification performance is evaluated using accuracy, sensitivity, specificity and F-measure. Widely-used classifiers, including feedforward neural network, support vector machine, k-nearest neighbours, AdaBoost and adaptive neuro-fuzzy inference system, are also implemented for comparisons. The wavelet-IT2FLS method considerably dominates the comparable classifiers on both datasets, and outperforms the best performance on the Ia and Ib datasets reported in the BCI competition II by 1.40% and 2.27% respectively. The proposed approach yields great accuracy and requires low computational cost, which can be applied to a real-time BCI system for motor imagery data analysis.

Monitoring and evaluating surgical care: defining perioperative mortality rate and standardising data collection

BACKGROUND: Case volume per 100 000 population and perioperative mortality rate (POMR) are key indicators to monitor and strengthen surgical services. However, comparisons of POMR have been restricted by absence of standardised approaches to when it is measured, the ideal denominator, need for risk adjustment, and whether data are available. We aimed to address these issues and recommend a minimum dataset by analysing four large mixed surgical datasets, two from well-resourced settings with sophisticated electronic patient information systems and two from resource-limited settings where clinicians maintain locally developed databases. METHODS: We obtained data from the New Zealand (NZ) National Minimum Dataset, the Geelong Hospital patient management system in Australia, and purpose-built surgical databases in Pietermaritzburg, South Africa (PMZ) and Port Moresby, Papua New Guinea (PNG). Information was sought on inclusion and exclusion criteria, coding criteria, and completeness of patient identifiers, admission, procedure, discharge and death dates, operation details, urgency of admission, and American Society of Anesthesiologists (ASA) score. Date-related errors were defined as missing dates and impossible discrepancies. For every site, we then calculated the POMR, the effect of admission episodes or procedures as denominator, and the difference between in-hospital POMR and 30-day POMR. To determine the need for risk adjustment, we used univariate and multivariate logistic regression to assess the effect on relative POMR for each site of age, admission urgency, ASA score, and procedure type. FINDINGS: 1 365 773 patient admissions involving 1 514 242 procedures were included, among which 8655 deaths were recorded within 30 days. Database inclusion and exclusion criteria differed substantially. NZ and Geelong records had less than 0·1% date-related errors and greater than 99·9% completeness. PMZ databases had 99·9% or greater completeness of all data except date-related items (94·0%). PNG had 99·9% or greater completeness for date of birth or age and admission date and operative procedure, but 80-83% completeness of patient identifiers and date related items. Coding of procedures was not standardised, and only NZ recorded ASA status and complete post-discharge mortality. In-hospital POMR range was 0·38% in NZ to 3·44% in PMZ, and in NZ it underestimated 30-day POMR by roughly a third. The difference in POMR by procedures instead of admission episodes as denominator ranged from 10% to 70%. Age older than 65 years and emergency admission had large independent effects on POMR, but relatively little effect in multivariate analysis on the relative odds of in-hospital death at each site. INTERPRETATION: Hospitals can collect and provide data for case volume and POMR without sophisticated electronic information systems. POMR should initially be defined by in-hospital mortality because post-discharge deaths are not usually recorded, and with procedures as denominator because details allowing linkage of several operations within one patient's admission are not always present. Although age and admission urgency are independently associated with POMR, and ASA and case mix were not included, risk adjustment might not be essential because the relative odds between sites persisted. Standardisation of inclusion criteria and definitions is needed, as is attention to accuracy and completeness of dates of procedures, discharge and death. A one-page, paper-based form, or alternatively a simple electronic data collection form, containing a minimum dataset commenced in the operating theatre could facilitate this process. FUNDING: None.

A stochastic time-domain model for burst data aggregation in IEEE 802.15.4 wireless sensor networks

In many network applications, the nature of traffic is of burst type. Often, the transient response of network to such traffics is the result of a series of interdependant events whose occurrence prediction is not a trivial task. The previous efforts in IEEE 802.15.4 networks often followed top-down approaches to model those sequences of events, i.e., through making top-view models of the whole network, they tried to track the transient response of network to burst packet arrivals. The problem with such approaches was that they were unable to give station-level views of network response and were usually complex. In this paper, we propose a non-stationary analytical model for the IEEE 802.15.4 slotted CSMA/CA medium access control (MAC) protocol under burst traffic arrival assumption and without the optional acknowledgements. We develop a station-level stochastic time-domain method from which the network-level metrics are extracted. Our bottom-up approach makes finding station-level details such as delay, collision and failure distributions possible. Moreover, network-level metrics like the average packet loss or transmission success rate can be extracted from the model. Compared to the previous models, our model is proven to be of lower memory and computational complexity order and also supports contention window sizes of greater than one. We have carried out extensive and comparative simulations to show the high accuracy of our model.

Barriers and enablers to healthcare access and use among Arabic-speaking and Caucasian English-speaking patients with type 2 diabetes mellitus: a qualitative comparative study

OBJECTIVE: The objective of this study was to explore the decision-making processes and associated barriers and enablers that determine access and use of healthcare services in Arabic-speaking and English-speaking Caucasian patients with diabetes in Australia. STUDY SETTING AND DESIGN: Face-to-face semistructured individual interviews and group interviews were conducted at various healthcare settings-diabetes outpatient clinics in 2 tertiary referral hospitals, 6 primary care practices and 10 community centres in Melbourne, Australia. PARTICIPANTS: A total of 100 participants with type 2 diabetes mellitus were recruited into 2 groups: 60 Arabic-speaking and 40 English-speaking Caucasian. DATA COLLECTION: Interviews were audio-taped, translated into English when necessary, transcribed and coded thematically. Sociodemographic and clinical information was gathered using a self-completed questionnaire and medical records. PRINCIPAL FINDINGS: Only Arabic-speaking migrants intentionally delayed access to healthcare services when obvious signs of diabetes were experienced, missing opportunities to detect diabetes at an early stage. Four major barriers and enablers to healthcare access and use were identified: influence of significant other(s), unique sociocultural and religious beliefs, experiences with healthcare providers and lack of knowledge about healthcare services. Compared with Arabic-speaking migrants, English-speaking participants had no reluctance to access and use medical services when signs of ill-health appeared; their treatment-seeking behaviours were straightforward. CONCLUSIONS: Arabic-speaking migrants appear to intentionally delay access to medical services even when symptomatic. Four barriers to health services access have been identified. Tailored interventions must be developed for Arabic-speaking migrants to improve access to available health services, facilitate timely diagnosis of diabetes and ultimately to improve glycaemic control.

Structured type 1 diabetes education delivered in routine care in Australia reduces diabetes-related emergencies and severe diabetes-related distress: the OzDAFNE program

AIMS: To evaluate structured type 1 diabetes education delivered in routine practice throughout Australia.

METHODS: Participants attended a five-day training program in insulin dose adjustment and carbohydrate counting between April 2007 and February 2012. Using an uncontrolled before-and-after study design, we investigated: HbA1c (% and mmol/mol); severe hypoglycaemia; diabetes ketoacidosis (DKA) requiring hospitalisation, and diabetes-related distress (Problem Areas in Diabetes scale; PAID), weight (kg); body mass index. Data were collected pre-training and 6-18 months post-training. Change in outcome scores were examined overall as well as between groups stratified by baseline HbA1c quartiles. Data are mean±SD or % (n).

RESULTS: 506 participants had data eligible for analysis. From baseline to follow-up, significant reductions were observed in the proportion of participants reporting at least one severe hypoglycaemic event (24.7% (n=123) vs 12.1% (n=59), p<0.001); and severe diabetes-related distress (29.3% (n=145) vs 12.6% (n=60), p<0.001). DKA requiring hospitalisation in the past year reduced from 4.1% (n=20) to 1.2% (n=6). For those with above target baseline HbA1c there was a small, statistically significant improvement (n=418, 8.4±1.1% (69±12mmol/mol) to 8.2±1.1% (66±12mmol/mol). HbA1c improvement was clinically significant among those in the highest baseline quartile (n=122, 9.7±1.1% (82±11mmol/mol) to 9.0±1.2% (75±13mmol/mol), p<0.001).

CONCLUSIONS: The proportion of participants reporting severe hypoglycaemia, DKA and severe diabetes-related distress was at least halved, and HbA1c reduced by 0.7% (7mmol/mol) among those with highest baseline levels. Structured type 1 diabetes education delivered in routine practice offers clinically important benefits for those with greatest clinical need.

Approximation of centroid end-points and switch points for replacing type reduction algorithms

Despite several years of research, type reduction (TR) operation in interval type-2 fuzzy logic system (IT2FLS) cannot perform as fast as a type-1 defuzzifier. In particular, widely used Karnik-Mendel (KM) TR algorithm is computationally much more demanding than alternative TR approaches. In this work, a data driven framework is proposed to quickly, yet accurately, estimate the output of the KM TR algorithm using simple regression models. Comprehensive simulation performed in this study shows that the centroid end-points of KM algorithm can be approximated with a mean absolute percentage error as low as 0.4%. Also, switch point prediction accuracy can be as high as 100%. In conjunction with the fact that simple regression model can be trained with data generated using exhaustive defuzzification method, this work shows the potential of proposed method to provide highly accurate, yet extremely fast, TR approximation method. Speed of the proposed method should theoretically outperform all available TR methods while keeping the uncertainty information intact in the process.

Privacy-preserving mechanism for social network data publishing

 Privacy is receiving growing concern from various parties especially consumers due to the simplification of the collection and distribution of personal data. This research focuses on preserving privacy in social network data publishing. The study explores the data anonymization mechanism in order to improve privacy protection of social network users. We identified new type of privacy breach and has proposed an effective mechanism for privacy protection.

Linear approximation of Karnik-Mendel type reduction algorithm

Karnik-Mendel (KM) algorithm is the most used and researched type reduction (TR) algorithm in literature. This algorithm is iterative in nature and despite consistent long term effort, no general closed form formula has been found to replace this computationally expensive algorithm. In this research work, we demonstrate that the outcome of KM algorithm can be approximated by simple linear regression techniques. Since most of the applications will have a fixed range of inputs with small scale variations, it is possible to handle those complexities in design phase and build a fuzzy logic system (FLS) with low run time computational burden. This objective can be well served by the application of regression techniques. This work presents an overview of feasibility of regression techniques for design of data-driven type reducers while keeping the uncertainty bound in FLS intact. Simulation results demonstrates the approximation error is less than 2%. Thus our work preserve the essence of Karnik-Mendel algorithm and serves the requirement of low
computational complexities.

Switch point finding using polynomial regression for fuzzy type reduction algorithms

Karnik-Mendel (KM) algorithm is the most widely used type reduction (TR) method in literature for the design of interval type-2 fuzzy logic systems (IT2FLS). Its iterative nature for finding left and right switch points is its Achilles heel. Despite a decade of research, none of the alternative TR methods offer uncertainty measures equivalent to KM algorithm. This paper takes a data-driven approach to tackle the computational burden of this algorithm while keeping its key features. We propose a regression method to approximate left and right switch points found by KM algorithm. Approximator only uses the firing intervals, rnles centroids, and FLS strnctural features as inputs. Once training is done, it can precisely approximate the left and right switch points through basic vector multiplications. Comprehensive simulation results demonstrate that the approximation accuracy for a wide variety of FLSs is 100%. Flexibility, ease of implementation, and speed are other features of the proposed method.