First-principle data-driven models for assessment of motor disorders in Parkinson’s disease

Parkinson’s disease (PD) is an increasing neurological disorder in an aging society. The motor and non-motor symptoms of PD advance with the disease progression and occur in varying frequency and duration. In order to affirm the full extent of a patient’s condition, repeated assessments are necessary to adjust medical prescription. In clinical studies, symptoms are assessed using the unified Parkinson’s disease rating scale (UPDRS). On one hand, the subjective rating using UPDRS relies on clinical expertise. On the other hand, it requires the physical presence of patients in clinics which implies high logistical costs. Another limitation of clinical assessment is that the observation in hospital may not accurately represent a patient’s situation at home. For such reasons, the practical frequency of tracking PD symptoms may under-represent the true time scale of PD fluctuations and may result in an overall inaccurate assessment. Current technologies for at-home PD treatment are based on data-driven approaches for which the interpretation and reproduction of results are problematic.  The overall objective of this thesis is to develop and evaluate unobtrusive computer methods for enabling remote monitoring of patients with PD. It investigates first-principle data-driven model based novel signal and image processing techniques for extraction of clinically useful information from audio recordings of speech (in texts read aloud) and video recordings of gait and finger-tapping motor examinations. The aim is to map between PD symptoms severities estimated using novel computer methods and the clinical ratings based on UPDRS part-III (motor examination). A web-based test battery system consisting of self-assessment of symptoms and motor function tests was previously constructed for a touch screen mobile device. A comprehensive speech framework has been developed for this device to analyze text-dependent running speech by: (1) extracting novel signal features that are able to represent PD deficits in each individual component of the speech system, (2) mapping between clinical ratings and feature estimates of speech symptom severity, and (3) classifying between UPDRS part-III severity levels using speech features and statistical machine learning tools. A novel speech processing method called cepstral separation difference showed stronger ability to classify between speech symptom severities as compared to existing features of PD speech. In the case of finger tapping, the recorded videos of rapid finger tapping examination were processed using a novel computer-vision (CV) algorithm that extracts symptom information from video-based tapping signals using motion analysis of the index-finger which incorporates a face detection module for signal calibration. This algorithm was able to discriminate between UPDRS part III severity levels of finger tapping with high classification rates. Further analysis was performed on novel CV based gait features constructed using a standard human model to discriminate between a healthy gait and a Parkinsonian gait. The findings of this study suggest that the symptom severity levels in PD can be discriminated with high accuracies by involving a combination of first-principle (features) and data-driven (classification) approaches. The processing of audio and video recordings on one hand allows remote monitoring of speech, gait and finger-tapping examinations by the clinical staff. On the other hand, the first-principles approach eases the understanding of symptom estimates for clinicians. We have demonstrated that the selected features of speech, gait and finger tapping were able to discriminate between symptom severity levels, as well as, between healthy controls and PD patients with high classification rates. The findings support suitability of these methods to be used as decision support tools in the context of PD assessment.

The influence of patient complexity and nurses` experience on haemodynamic decision-making following cardiac surgery

Critical care nurses’ haemodynamic decision-making in the immediate postoperative cardiac surgical context is complex. To optimise patient outcomes, nurses of varying levels of experience are required to make complex decisions rapidly and accurately. In a dynamic clinical context such as critical care, the quality of such decision-making is likely to vary considerably. The aim of this study was to describe variability of nurses’ haemodynamic decision-making in the 2-hour period after cardiac surgery as a function of interplay between decision complexity, nurses’ levels of experience, and the support provided. A descriptive study based on naturalistic decision-making was used. Data were collected using continuous non-participant observation of clinical practice for a 2-hour period and follow-up interview. Purposive sampling was used to recruit 38 nurses for inclusion in the study. The quality of nurses’ decision-making was influenced by interplay between the complexity of patients’ haemodynamic presentations, nurses’ levels of cardiac surgical intensive care experience, and the form of decision support provided by nursing colleagues. Two factors specifically influenced decision-making quality: nurses’ utilisation of evidence for practice and the experience levels of both nurses and their colleagues. The findings have implications for staff resourcing decisions and postoperative patient management, and may be used to inform nurses’ professional development and education.

A knowledge management approach to user support

This paper considers the problem of computer user support and workplace learning in general. Theoretically our work is influenced by ideas on knowledge management, expertise networks and communities of practice. Our approach seeks to tap into the powerful and situated learning potential of the collaborative support provided by colleagues. We consider that such support could be enhanced through the use of a collaborative support system. We outline our investigations into design issues, a generic model and various experiments related to the development of such a system. In particular, we emphasise the value of recorded demonstrations for representing computer-related practice. We present a number of design conclusions derived from our experiences, and warn that whereas active user participation is the essential ingredient in a support system it is perhaps the most difficult thing to achieve.

Simulation-based support tool for operator performance in batch manufacturing

In industry, the workload and utilization of shop floor operators is often misunderstood. In this paper, we will present several real case studies, using Discrete Event Simulation (DES) models, which allow us to better understand operators in a batch manufacturing environment. The first study investigates labour in a machining plant consisting of multiple identical CNC machines that batch produce parts. The second study investigates labour in an eight station, gravity die casting rotary table. The results from these studies have shown that there can be potential improvements made by the production planners in the current labour configuration. In the first case study, a matrix is produced that estimates what the operator's utilization levels will be for various configurations. From this, the preferred operator to machine ratio over a range of cycle times is presented. In the second study, the results have shown that by reducing the casting cycle time, the operator would be overloaded. A discrete event simulation of these two cases highlighted areas that were misunderstood by plant management, and provided them with a useful decision support tool for production planning.

Exploring creativity support systems for the NE"X"T generation

This paper explores the implementation of a creativity support system for tertiary students studying games design and development at Deakin University, Victoria, Australia. The students at the centre of this study are the ‘next’ generation of learners and are often called the net generation because of their pre-imposed affinity for all things ‘online’. The creativity support system for the games students is designed within a ‘whole’ systems context. Focusing on only one tool to augment a person’s creativity does not take into consideration social factors that are pertinent on a person ability to grow their creative behaviours. This study will present a set of factors that each creativity support system should employ to facilitate creative abilities within people, with particular focus on how social activities help to enhance creativity.

Analysis of forest policy using multi-attribute value theory

Contents:
1. Role of multi-criteria decision making in natural resource management /​ Gamini Herath and Tony Prato
2. Analysis of forest policy using multi-attribute value theory /​ Jayanath Ananda and Gamini Herath
3. Comparing Riparian revegetation policy options using the analytic hierarchy process /​ M. E. Qureshi and S. R. Harrison
4. Managing environmental and health risks from a lead and zinc smelter : an application of deliberative multi-criteria evaluation /​ Wendy Proctor, Chris McQuade and Anne Dekker
5. Multiple attribute evaluation of management alternatives for the Missouri River System /​ Tony Prato
6. Multi-criteria decision analysis for integrated watershed management /​ Zeyuan Qiu
7. Fuzzy multiple attribute evaluation of agricultural systems /​ Leonie A. Marks and Elizabeth G. Dunn
8. Multi-criteria decision support for energy supply assessment /​ Bram Noble
9. Seaport development in Vietnam : evaluation using the analytic hierarchy process /​ Tran Phuong Dong and David M. Chapman
10. Valuing wetland aquatic resources using the analytic hierarchy process /​ Premachandra Wattage and Simon Mardle
11. Multiple attribute evaluation for national park management /​ Tony Prato
12. The future of MCDA in natural resource management : some generalizations /​ Gamini Herath and Tony Prato.

Decision making effectiveness in wildfire incident management teams

During large scale wildfires, suppression activities are carried out under the direction of an Incident Management Team (IMT). The aim of the research was to increase understanding of decision processes potentially related to IMT effectiveness. An IMT comprises four major functions: Command, Operations, Planning, and Logistics. Four methodologies were used to study IMT processes: computer simulation experiments; analyses of wildfire reports; interviews with IMT members; and cognitive ethnographic studies of IMTs. Three processes were important determinants of IMT effectiveness: information management and cognitive overload; matching component function goals to overall goals; and team metacognition to detect and counter task-disruptive developments. These processes appear to be complex multi-person analogues of individual Incident Command processes identified previously. The findings have implications for issues such as: creating IMTs; training IMTs; managing IMTs; and providing decision support to IMTs.

Discrepancy between expected and actual child support payments : predicting the health and health-related quality of life of children living in low-income, single-parent families

Objective Although the amount and frequency of child support payments received by single parents are often erratic and fluctuate, no study to date has quantitatively explored how the discrepancy between expected and actual payments relates to child health. This study aims to examine whether the discrepancy between expected and actual child support payments predicts a range of child health outcomes, including global health, health-related quality of life, involvement in activities and parental psychological distress.

Methods This study used results from the Longitudinal Study of Australian Children, which included a sample of parents of children aged 4–5 years (n = 4983). The questionnaire was completed by the parent who spent the most time with the child and knew the child best. From the 4983 families, 332 low-income single parents reliant on welfare with a formal or informal child support order in place were identified.

Results After controlling for income, the discrepancy between expected and actual child support predicted school functioning, conduct problems, total mental health problems and involvement in activities. Discrepancy between expected and actual child support payments did not predict the remaining health-related quality of life domains, mental health domains, global child health or parental psychosocial distress.

Conclusion This was the first study to examine how the discrepancy between expected and actual child support payments relates to child health, providing important data on the effectiveness of the child support system for children's well-being. These findings highlight the potential impact of the discrepancy on school functioning, conduct problems, total mental health problems and involvement in activities.

Exploring creativity support systems for the Ne"X"t generation

This paper explores the implementation of a creativity support system (CSS) for tertiary students studying Games Design and Development at Deakin University, Victoria, Australia. The students at the centre of this study are the ‘next’ generation of learners and are often called the Internet generation because of their pre-imposed for ‘online’ and being ‘connected’. The CSS for the games students is designed within a context that encompasses a ‘whole’ system, as focusing on only one component to augment a person’s creativity does not take into consideration the multitude of factors, for example social factors, that are pertinent on a person ability to grow their creative behaviours. This study will present a set of factors that each CSS should employ to facilitate creative abilities within people, with particular focus on how social activities help to enhance creativity.

Nurses' critical event risk assessments : a judgement analysis

Aims. To explore and explain nurses' use of readily available clinical information when deciding whether a patient is at risk of a critical event.

Background. Half of inpatients who suffer a cardiac arrest have documented but unacted upon clinical signs of deterioration in the 24 hours prior to the event. Nurses appear to be both misinterpreting and mismanaging the nursing-knowledge 'basics' such as heart rate, respiratory rate and oxygenation. Whilst many medical interventions originate from nurses, up to 26% of nurses' responses to abnormal signs result in delays of between one and three hours.

Methods. A double system judgement analysis using Brunswik's lens model of cognition was undertaken with 245 Dutch, UK, Canadian and Australian acute care nurses. Nurses were asked to judge the likelihood of a critical event, 'at-risk' status, and whether they would intervene in response to 50 computer-presented clinical scenarios in which data on heart rate, systolic blood pressure, urine output, oxygen saturation, conscious level and oxygenation support were varied. Nurses were also presented with a protocol recommendation and also placed under time pressure for some of the scenarios. The ecological criterion was the predicted level of risk from the Modified Early Warning Score assessments of 232 UK acute care inpatients.

Results. Despite receiving identical information, nurses varied considerably in their risk assessments. The differences can be partly explained by variability in weightings given to information. Time and protocol recommendations were given more weighting than clinical information for key dichotomous choices such as classifying a patient as 'at risk' and deciding to intervene. Nurses' weighting of cues did not mirror the same information's contribution to risk in real patients. Nurses synthesized information in non-linear ways that contributed little to decisional accuracy. The low-moderate achievement (Ra) statistics suggests that nurses' assessments of risk were largely inaccurate; these assessments were applied consistently among 'patients' (scenarios). Critical care experience was statistically associated with estimates of risk, but not with the decision to intervene.

Conclusion. Nurses overestimated the risk and the need to intervene in simulated paper patients at risk of a critical event. This average response masked considerable variation in risk predictions, the need for action and the weighting afforded to the information they had available to them. Nurses did not make use of the linear reasoning required for accurate risk predictions in this task. They also failed to employ any unique knowledge that could be shown to make them more accurate. The influence of time pressure and protocol recommendations depended on the kind of judgement faced suggesting then that knowing more about the types of decisions nurses face may influence information use.

Relevance to clinical practice. Practice developers and educators need to pay attention to the quality of nurses' clinical experience as well as the quantity when developing judgement expertise in nurses. Intuitive unaided decision making in the assessment of risk may not be as accurate as supported decision making. Practice developers and educators should consider teaching nurses normative rules for revising probabilities (even subjective ones) such as Bayes' rule for diagnostic or assessment judgements and also that linear ways of thinking, in which decision support may help, may be useful for many choices that nurses face. Nursing needs to separate the rhetoric of 'holism' and 'expertise' from the science of predictive validity, accuracy and competence in judgement and decision making.

Burnout in physicians : a case for peer-support

Background: It is well documented that doctors experience a high level of stress in their profession, and that this can lead to physical, psychological, and emotional harm, in particular, burnout. Overseas (especially in the UK and USA), research investigating the levels of stress, burnout, and associated psychiatric morbidity in health professionals, across many specialities, has been carried out with a view to prevention of these adverse outcomes.

Aims: To assess the level of burnout in a sample of New Zealand physicians, the associated work and personal characteristics, and the need for development of a support peer supervision or support system.

Methods: Questionnaires that measured a number of work and personal characteristics, including the Maslach Burnout Inventory, the General Health Questionnaire, and additional questions regarding mistakes, and need for support, were sent to 83 physicians in the Waikato and Bay of Plenty areas. Analysis involved descriptive statistics, with t-tests for comparison with other studies, Pearson Product-Moment correlations between variables and analysis of variance where appropriate.

Results: Of the 50 respondents, 28% experienced high levels of two or three aspects of burnout (emotional exhaustion, depersonalization, low personal accomplishment). Emotional exhaustion correlated with a greater need for support. Most respondents favoured a one-to-one support system.

Conclusion: This study highlights the presence of significant workplace difficulties for physicians and the need to develop a preventative support system for the protection of physicians and the patients in their care.

How information technology can support family law and mediation

In Australia, before a divorcing couple can have their case heard by the Family Court, they must undertake mediation. Thus it is useful to develop information technology tools to support negotiation and mediation in family law. Most negotiation support systems focus upon integrative bargaining. In doing so, they tend to ignore issues of fairness. In Australian Family Law, the interests of the children, as opposed to those of their parents/guardians, are paramount. We investigate the use of providing BATNAs and integrative bargaining in providing family mediation decision support. The discussion is highlighted with examples taken from the domain of Australian Family Law

Strategic facilities management system in relation with building performance; the significance and relationship

Healthcare design frequently involves complex concepts that are difficult to measure and evaluate because the building require a modern, quality, functional and therapeutic environment. For this specific reason, facilities management has become a very important support system to ensure smoothness in healthcare business. Facilities management in healthcare building is a complicated system involving multiple layers of administrative division and sub-divisions. Building performance such as building impact, function and quality prove to have significant impact on strategic facilities management. This paper will do an extensive review of strategic healthcare business management as a holistic approach and examine how facilities management can effectively manage their division with consideration and understanding of building performance. The correlation between strategic facilities management and building performance will be identified and a framework for strategic FM system with regards to building performance will be developed.

Anaesthesia monitoring using fuzzy logic

Objective. Humans have a limited ability to accurately and continuously analyse large amount of data. In recent times, there has been a rapid growth in patient monitoring and medical data analysis using smart monitoring systems. Fuzzy logic-based expert systems, which can mimic human thought processes in complex circumstances, have indicated potential to improve clinicians' performance and accurately execute repetitive tasks to which humans are ill-suited. The main goal of this study is to develop a clinically useful diagnostic alarm system based on fuzzy logic for detecting critical events during anaesthesia administration. Method. The proposed diagnostic alarm system called fuzzy logic monitoring system (FLMS) is presented. New diagnostic rules and membership functions (MFs) are developed. In addition, fuzzy inference system (FIS), adaptive neuro fuzzy inference system (ANFIS), and clustering techniques are explored for developing the FLMS' diagnostic modules. The performance of FLMS which is based on fuzzy logic expert diagnostic systems is validated through a series of offline tests. The training and testing data set are selected randomly from 30 sets of patients' data. Results. The accuracy of diagnoses generated by the FLMS was validated by comparing the diagnostic information with the one provided by an anaesthetist for each patient. Kappa-analysis was used for measuring the level of agreement between the anaesthetist's and FLMS's diagnoses. When detecting hypovolaemia, a substantial level of agreement was observed between FLMS and the human expert (the anaesthetist) during surgical procedures. Conclusion. The diagnostic alarm system FLMS demonstrated that evidence-based expert diagnostic systems can diagnose hypovolaemia, with a substantial degree of accuracy, in anaesthetized patients and could be useful in delivering decision support to anaesthetists.