Model selection and error estimation

We study model selection strategies based on penalized empirical loss minimization. We point out a tight relationship between error estimation and data-based complexity penalization: any good error estimate may be converted into a data-based penalty function and the performance of the estimate is governed by the quality of the error estimate. We consider several penalty functions, involving error estimates on independent test data, empirical VC dimension, empirical VC entropy, and margin-based quantities. We also consider the maximal difference between the error on the first half of the training data and the second half, and the expected maximal discrepancy, a closely related capacity estimate that can be calculated by Monte Carlo integration. Maximal discrepancy penalty functions are appealing for pattern classification problems, since their computation is equivalent to empirical risk minimization over the training data with some labels flipped.

Are low CO2 emitters in rural areas also socially excluded? An empirical investigation of travel diary data from rural Northern Ireland

The reduction of CO2 emissions and social exclusion are two key elements of UK transport strategy. Despite intensive research on each theme, little effort has so far been made linking the relationship between emissions and social exclusion. In addition, current knowledge on each theme is limited to urban areas; little research is available on these themes for rural areas. This research contributes to this gap in the literature by analysing 157 weekly activity-travel diary data collected from three case study areas with differential levels of area accessibility and area mobility options, located in rural Northern Ireland. Individual weekly CO2 emission levels from personal travel diaries (both hot exhaust emission and cold-start emission) were calculated using average speed models for different modes of transport. The socio-spatial patterns associated with CO2 emissions were identified using a general linear model whereas binary logistic regression analyses were conducted to identify mode choice behaviour and activity patterns. This research found groups that emitted a significantly lower level of CO2 included individuals living in an area with a higher level of accessibility and mobility, non-car, non-working, and low-income older people. However, evidence in this research also shows that although certain groups (e.g. those working, and residing in an area with a lower level of accessibility) emitted higher levels of CO2, their rate of participation in activities was however found to be significantly lower compared to their counterparts. Based on the study findings, this research highlights the need for both soft (e.g. teleworking) and physical (e.g. accessibility planning) policy measures in rural areas in order to meet government’s stated CO2 reduction targets while at the same time enhancing social inclusion.

The WIL (Work Integrated Learning) report : a national scoping study [Final Report]

This report provides an account of the first large-scale scoping study of work integrated learning (WIL) in contemporary Australian higher education. The explicit aim of the project was to identify issues and map a broad and growing picture of WIL across Australia and to identify ways of improving the student learning experience in relation to WIL. The project was undertaken in response to high levels of interest in WIL, which is seen by universities both as a valid pedagogy and as a means to respond to demands by employers for work-ready graduates, and demands by students for employable knowledge and skills. Over a period of eight months of rapid data collection, 35 universities and almost 600 participants contributed to the project. Participants consistently reported the positive benefits of WIL and provided evidence of commitment and innovative practice in relation to enhancing student learning experiences. Participants provided evidence of strong partnerships between stakeholders and highlighted the importance of these relationships in facilitating effective learning outcomes for students. They also identified a range of issues and challenges that face the sector in growing WIL opportunities; these issues and challenges will shape the quality of WIL experiences. While the majority of comments focused on issues involved in ensuring quality placements, it was recognised that placements are just one way to ensure the integration of work with learning. Also, the WIL experience is highly contextualised and impacted by the expectations of students, employers, the professions, the university and government policy.

Utility of routine data sources for feedback on the quality of cancer care: an assessment based on clinical practice guidelines

Background Not all cancer patients receive state-of-the-art care and providing regular feedback to clinicians might reduce this problem. The purpose of this study was to assess the utility of various data sources in providing feedback on the quality of cancer care. Methods Published clinical practice guidelines were used to obtain a list of processes-of-care of interest to clinicians. These were assigned to one of four data categories according to their availability and the marginal cost of using them for feedback. Results Only 8 (3%) of 243 processes-of-care could be measured using population-based registry or administrative inpatient data (lowest cost). A further 119 (49%) could be measured using a core clinical registry, which contains information on important prognostic factors (e.g., clinical stage, physiological reserve, hormone-receptor status). Another 88 (36%) required an expanded clinical registry or medical record review; mainly because they concerned long-term management of disease progression (recurrences and metastases) and 28 (11.5%) required patient interview or audio-taping of consultations because they involved information sharing between clinician and patient. Conclusion The advantages of population-based cancer registries and administrative inpatient data are wide coverage and low cost. The disadvantage is that they currently contain information on only a few processes-of-care. In most jurisdictions, clinical cancer registries, which can be used to report on many more processes-of-care, do not cover smaller hospitals. If we are to provide feedback about all patients, not just those in larger academic hospitals with the most developed data systems, then we need to develop sustainable population-based data systems that capture information on prognostic factors at the time of initial diagnosis and information on management of disease progression.

On the data consumption benefits of accepting increased uncertainty

In the context of learning paradigms of identification in the limit, we address the question: why is uncertainty sometimes desirable? We use mind change bounds on the output hypotheses as a measure of uncertainty and interpret ‘desirable’ as reduction in data memorization, also defined in terms of mind change bounds. The resulting model is closely related to iterative learning with bounded mind change complexity, but the dual use of mind change bounds — for hypotheses and for data — is a key distinctive feature of our approach. We show that situations exist where the more mind changes the learner is willing to accept, the less the amount of data it needs to remember in order to converge to the correct hypothesis. We also investigate relationships between our model and learning from good examples, set-driven, monotonic and strong-monotonic learners, as well as class-comprising versus class-preserving learnability.

The impact of nonlinear exposure risk relationships on seasonal time series data: modelling danish neonatal birth anthropometric data

Background Birth weight and length have seasonal fluctuations. Previous analyses of birth weight by latitude effects identified seemingly contradictory results, showing both 6 and 12 monthly periodicities in weight. The aims of this paper are twofold: (a) to explore seasonal patterns in a large, Danish Medical Birth Register, and (b) to explore models based on seasonal exposures and a non-linear exposure-risk relationship. Methods Birth weight and birth lengths on over 1.5 million Danish singleton, live births were examined for seasonality. We modelled seasonal patterns based on linear, U- and J-shaped exposure-risk relationships. We then added an extra layer of complexity by modelling weighted population-based exposure patterns. Results The Danish data showed clear seasonal fluctuations for both birth weight and birth length. A bimodal model best fits the data, however the amplitude of the 6 and 12 month peaks changed over time. In the modelling exercises, U- and J-shaped exposure-risk relationships generate time series with both 6 and 12 month periodicities. Changing the weightings of the population exposure risks result in unexpected properties. A J-shaped exposure-risk relationship with a diminishing population exposure over time fitted the observed seasonal pattern in the Danish birth weight data. Conclusion In keeping with many other studies, Danish birth anthropometric data show complex and shifting seasonal patterns. We speculate that annual periodicities with non-linear exposure-risk models may underlie these findings. Understanding the nature of seasonal fluctuations can help generate candidate exposures.

Rural and Urban Differentials in Primary Care Management of Chronic Heart Failure: New Data from the CASE Study

Objective: To determine whether primary care management of chronic heart failure (CHF) differed between rural and urban areas in Australia. Design: A cross-sectional survey stratified by Rural, Remote and Metropolitan Areas (RRMA) classification. The primary source of data was the Cardiac Awareness Survey and Evaluation (CASE) study. Setting: Secondary analysis of data obtained from 341 Australian general practitioners and 23 845 adults aged 60 years or more in 1998. Main outcome measures: CHF determined by criteria recommended by the World Health Organization, diagnostic practices, use of pharmacotherapy, and CHF-related hospital admissions in the 12 months before the study. Results: There was a significantly higher prevalence of CHF among general practice patients in large and small rural towns (16.1%) compared with capital city and metropolitan areas (12.4%) (P < 0.001). Echocardiography was used less often for diagnosis in rural towns compared with metropolitan areas (52.0% v 67.3%, P < 0.001). Rates of specialist referral were also significantly lower in rural towns than in metropolitan areas (59.1% v 69.6%, P < 0.001), as were prescribing rates of angiotensin-converting enzyme inhibitors (51.4% v 60.1%, P < 0.001). There was no geographical variation in prescribing rates of β-blockers (12.6% [rural] v 11.8% [metropolitan], P = 0.32). Overall, few survey participants received recommended “evidence-based practice” diagnosis and management for CHF (metropolitan, 4.6%; rural, 3.9%; and remote areas, 3.7%). Conclusions: This study found a higher prevalence of CHF, and significantly lower use of recommended diagnostic methods and pharmacological treatment among patients in rural areas.

Assessing the concordance of health and child protection data for 'maltreated' and 'unintentionally injured' children

Objectives: To quantify the concordance of hospital child maltreatment data with child protection service (CPS) records and identify factors associated with linkage. Methods: Multivariable logistic regression analysis was conducted following retrospective medical record review and database linkage of 884 child records from 20 hospitals and the CPS in Queensland, Australia. Results: Nearly all children with hospital assigned maltreatment codes (93.1%) had a CPS record. Of these, 85.1% had a recent notification. 29% of the linked maltreatment group (n=113) were not known to CPS prior to the hospital presentation. Almost 1/3 of children with unintentional injury hospital codes were known to CPS. Just over 24% of the linked unintentional injury group (n=34) were not known to CPS prior to the hospital presentation but became known during or after discharge from hospital. These estimates are higher than the 2006/07 annual rate of 2.39% of children being notified to CPS. Rural children were more likely to link to CPS, and children were over 3 times more likely to link if the index injury documentation included additional diagnoses or factors affecting their health. Conclusions: The system for referring maltreatment cases to CPS is generally efficient, although up to 1 in 15 children had codes for maltreatment but could not be linked to CPS data. The high proportion of children with unintentional injury codes who linked to CPS suggests clinicians and hospital-based child protection staff should be supported by further education and training to ensure children at risk are being detected by the child protection system.