A comparison of methods for classifying clinical samples based on proteomics data : a case study for statistical and machine learning approaches

The discovery of protein variation is an important strategy in disease diagnosis within the biological sciences. The current benchmark for elucidating information from multiple biological variables is the so called “omics” disciplines of the biological sciences. Such variability is uncovered by implementation of multivariable data mining techniques which come under two primary categories, machine learning strategies and statistical based approaches. Typically proteomic studies can produce hundreds or thousands of variables, p, per observation, n, depending on the analytical platform or method employed to generate the data. Many classification methods are limited by an n≪p constraint, and as such, require pre-treatment to reduce the dimensionality prior to classification. Recently machine learning techniques have gained popularity in the field for their ability to successfully classify unknown samples. One limitation of such methods is the lack of a functional model allowing meaningful interpretation of results in terms of the features used for classification. This is a problem that might be solved using a statistical model-based approach where not only is the importance of the individual protein explicit, they are combined into a readily interpretable classification rule without relying on a black box approach. Here we incorporate statistical dimension reduction techniques Partial Least Squares (PLS) and Principal Components Analysis (PCA) followed by both statistical and machine learning classification methods, and compared them to a popular machine learning technique, Support Vector Machines (SVM). Both PLS and SVM demonstrate strong utility for proteomic classification problems.

Sustaining teacher professionalism in the context of standards referenced assessment reform

Standards referenced reform, tied to reporting, engages directly with assessment issues related to accountability. Assessment is the key to good education and is inseparable from curriculum. In an accountability context, standards are used as a lever to improve the reliability and consistency of teacher judgement; and classroom evidence is used by education systems for reporting and tracking achievement over time. Assessment is thus a powerful driver for change and is at the heart of the teaching-learning dynamic. The relationship between the learner, learning and assessment needs to be kept central and the idea of teacher empowerment is fundamental. This chapter is a call to honour and sustain teacher professionalism through educative forms of school-based and teacher-led evaluation, assessment and communities of judgement practice. It supports the argument for a central place for classroom assessment in the role of assessment in educational accountability...

Workflow patterns put into context

In his paper “Approaches to Modeling Business Processes. A Critical Analysis of BPMN, Workflow Patterns and YAWL”, Egon Börger criticizes the work of the Workflow Patterns Initiative in a rather provocative manner. Although the workflow patterns and YAWL are well established and frequently used, Börger seems to misunderstand the goals and contributions of the Workflow Patterns Initiative. Therefore, we put the workflow patterns and YAWL in their historic context. Moreover, we address some of the criticism of Börger by pointing out the real purpose of the workflow patterns and their relationship to formal languages (Petri nets) and real-life WFM/BPM systems.

Stakeholders' perceptions of data utility in the context of ERP outcomes

A substantial body of literature exists identifying factors contributing to under-performing Enterprise Resource Planning systems (ERPs), including poor communication, lack of executive support and user dissatisfaction (Calisir et al., 2009). Of particular interest is Momoh et al.’s (2010) recent review identifying poor data quality (DQ) as one of nine critical factors associated with ERP failure. DQ is central to ERP operating processes, ERP facilitated decision-making and inter-organizational cooperation (Batini et al., 2009). Crucial in ERP contexts is that the integrated, automated, process driven nature of ERP data flows can amplify DQ issues, compounding minor errors as they flow through the system (Haug et al., 2009; Xu et al., 2002). However, the growing appreciation of the importance of DQ in determining ERP success lacks research addressing the relationship between stakeholders’ requirements and perceptions of ERP DQ, perceived data utility and the impact of users’ treatment of data on ERP outcomes.

Is the iPhone a useful clinical tool to monitor spinal deformity patients?

The progression of spinal deformity is traditionally monitored on hard copy radiographs using the Cobb method with a protractor and pencil. The rotation of the spine and ribcage (rib hump) in scoliosis is measured with a hand-held inclinometer/Scoliometer. The iPhone and other smart phones, can accurately sense inclination, and can therefore be used to measure Cobb angles and rib hump angulation. The purpose of this study was to quantify the performance of the iPhone compared to the standard protractor (Cobb angles) and the Scoliometer (rib hump).

A survey to evaluate the implementation of a national clinical assessment form

Purpose: The Australian Universities Radiation Therapy Student Clinical Assessment Form (AURTSCAF) was designed to assess the clinical skills of radiation therapy (RT) students from the six universities that offer entry level RT programs. Given the AURTSCAF has now been in use for over two years, the Radiation Therapy Program Coordinators (RTPC) group initiated a post implementation evaluation survey. This formed the final phase of the AURTSCAF project and was funded by the Radiation Oncology Division of the Department of Health and Ageing. Methods: A cross-sectional designed survey using purposive sampling was distributed via email to all RT clinical sites. The survey asked questions about the requirements of a pass grade for students at different stages of their program, and the addition of a new category of assessment related to fitness to practise. Response types included both forced choice closed ended responses and open ended responses. There was also a section for open comments about the AURTSCAF. Results: There were 100 responses (55%) from clinicians who had utilised the assessment form over the previous 12 month period. Responses highlighted several positives with regard to the utility and implementation of the form. Comments regarding areas for improvement with the standardisation of the grading of students and consensus for the addition of a new domain in fitness for practise have informed the recommended changes proposed for 2012. Conclusion: This evaluation has provided a representative sample of the views of clinicians involved in assessing students on clinical placement. Recommendations include the addition of the sixth domain of assessment: Fitness for practise, the addition of descriptors and prompts for this domain in the user guide, the addition of a consensus statement about the use of the rating scale and dissemination of the proposed changes nationally.

Bayesian hierarchical models in statistical quality control methods to improve healthcare in hospitals

Quality oriented management systems and methods have become the dominant business and governance paradigm. From this perspective, satisfying customers’ expectations by supplying reliable, good quality products and services is the key factor for an organization and even government. During recent decades, Statistical Quality Control (SQC) methods have been developed as the technical core of quality management and continuous improvement philosophy and now are being applied widely to improve the quality of products and services in industrial and business sectors. Recently SQC tools, in particular quality control charts, have been used in healthcare surveillance. In some cases, these tools have been modified and developed to better suit the health sector characteristics and needs. It seems that some of the work in the healthcare area has evolved independently of the development of industrial statistical process control methods. Therefore analysing and comparing paradigms and the characteristics of quality control charts and techniques across the different sectors presents some opportunities for transferring knowledge and future development in each sectors. Meanwhile considering capabilities of Bayesian approach particularly Bayesian hierarchical models and computational techniques in which all uncertainty are expressed as a structure of probability, facilitates decision making and cost-effectiveness analyses. Therefore, this research investigates the use of quality improvement cycle in a health vii setting using clinical data from a hospital. The need of clinical data for monitoring purposes is investigated in two aspects. A framework and appropriate tools from the industrial context are proposed and applied to evaluate and improve data quality in available datasets and data flow; then a data capturing algorithm using Bayesian decision making methods is developed to determine economical sample size for statistical analyses within the quality improvement cycle. Following ensuring clinical data quality, some characteristics of control charts in the health context including the necessity of monitoring attribute data and correlated quality characteristics are considered. To this end, multivariate control charts from an industrial context are adapted to monitor radiation delivered to patients undergoing diagnostic coronary angiogram and various risk-adjusted control charts are constructed and investigated in monitoring binary outcomes of clinical interventions as well as postintervention survival time. Meanwhile, adoption of a Bayesian approach is proposed as a new framework in estimation of change point following control chart’s signal. This estimate aims to facilitate root causes efforts in quality improvement cycle since it cuts the search for the potential causes of detected changes to a tighter time-frame prior to the signal. This approach enables us to obtain highly informative estimates for change point parameters since probability distribution based results are obtained. Using Bayesian hierarchical models and Markov chain Monte Carlo computational methods, Bayesian estimators of the time and the magnitude of various change scenarios including step change, linear trend and multiple change in a Poisson process are developed and investigated. The benefits of change point investigation is revisited and promoted in monitoring hospital outcomes where the developed Bayesian estimator reports the true time of the shifts, compared to priori known causes, detected by control charts in monitoring rate of excess usage of blood products and major adverse events during and after cardiac surgery in a local hospital. The development of the Bayesian change point estimators are then followed in a healthcare surveillances for processes in which pre-intervention characteristics of patients are viii affecting the outcomes. In this setting, at first, the Bayesian estimator is extended to capture the patient mix, covariates, through risk models underlying risk-adjusted control charts. Variations of the estimator are developed to estimate the true time of step changes and linear trends in odds ratio of intensive care unit outcomes in a local hospital. Secondly, the Bayesian estimator is extended to identify the time of a shift in mean survival time after a clinical intervention which is being monitored by riskadjusted survival time control charts. In this context, the survival time after a clinical intervention is also affected by patient mix and the survival function is constructed using survival prediction model. The simulation study undertaken in each research component and obtained results highly recommend the developed Bayesian estimators as a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances as well as industrial and business contexts. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The empirical results and simulations indicate that the Bayesian estimators are a strong alternative in change point estimation within quality improvement cycle in healthcare surveillances. The superiority of the proposed Bayesian framework and estimators are enhanced when probability quantification, flexibility and generalizability of the developed model are also considered. The advantages of the Bayesian approach seen in general context of quality control may also be extended in the industrial and business domains where quality monitoring was initially developed.

Evaluation of coronary angiographic projections to balance the clinical yield with the radiation risk

Objective: Radiation safety principles dictate that imaging procedures should minimise the radiation risks involved, without compromising diagnostic performance. This study aims to define a core set of views that maximises clinical information yield for minimum radiation risk. Angiographers would supplement these views as clinically indicated. Methods: An algorithm was developed to combine published data detailing the quality of information derived for the major coronary artery segments through the use of a common set of views in angiography with data relating to the dose–area product and scatter radiation associated with these views. Results: The optimum view set for the left coronary system comprised four views: left anterior oblique (LAO) with cranial (Cr) tilt, shallow right anterior oblique (AP-RAO) with caudal (Ca) tilt, RAO with Ca tilt and AP-RAO with Cr tilt. For the right coronary system three views were identified: LAO with Cr tilt, RAO and AP-RAO with Cr tilt. An alternative left coronary view set including a left lateral achieved minimally superior efficiency (,5%), but with an ,8% higher radiation dose to the patient and 40% higher cardiologist dose. Conclusion: This algorithm identifies a core set of angiographic views that optimises the information yield and minimises radiation risk. This basic data set would be supplemented by additional clinically determined views selected by the angiographer for each case. The decision to use additional views for diagnostic angiography and interventions would be assisted by referencing a table of relative radiation doses for the views being considered.

Professional development in the contemporary educational context : encouraging practice wisdom

Preparing social work students to be effective practitioners is a complex and challenging task undertaken in a dynamic environment both in terms of the field of social work and the higher education sector. There have been recommendations that self knowledge, empirical knowledge, theoretical knowledge and procedural knowledge are the keys to high standards of social work practice. This paper suggests that the concept of practice wisdom is a useful focus for integrating these different aspects of informed practice and for focusing educational programmes for social work. As practice wisdom is more about process than possessed characteristics then there are important motivational and value-based considerations in developing wise practitioners. This discussion considers motivational and personal narrative aspects of practice wisdom so that it can be integrated into social work teaching.