Finite element modeling and analysis of thrombosis in middle cerebral artery

Thrombotic stroke, which is caused by blood clot in the cerebral artery, is a major source of increased mortality and morbidity. Considering as efficient and fastest methods, mathematical approaches have gained significant importance for analyzing and understanding the biological events like thrombosis. This paper presents a computational model to analyze the effects of thrombosis using the theory of coupled fluid dynamics-structure interaction. The finite element method is used for the modeling of thrombosis (blood clot) of different stages in the middle cerebral artery with physiological compliance. The developed model is used to investigate the consequences that occur due to the various sizes of clots in the artery in the form of blood flow velocity, blood pressure, and artery wall stress. Such numerical assessment will facilitate better understanding of the biophysical process in case of thrombosis and thus would support medical practitioners to take faster curing steps.

Responsible management of motor vehicle drivers with dementia

When Bridget Driscoll, a 44-year-old mother of two died after being struck by a motor vehicle, considered to be the first motor vehicle fatality in UK and possibly the world, the coroner stated 'I trust this sort of nonsense will never happen again'.1 Sadly, the coroner, medical practitioners and general public would be deeply and repeatedly disappointed. It was 1896. Motor vehicles were a curiosity. Drivers did not undergo any form of testing, be it medical fitness, driving ability or otherwise, and there were no licensing regulatory agencies. By 2010, road injury was the ninth most common cause of death globally (1.3 million deaths per annum) and dementia the fourth most common in high income countries.2 By 2030 the number of all licensed UK drivers who are 65 years or older will increase by almost 50% to almost one in every four drivers.3 If the juxtaposition of driving with dementia in an ageing population is not already a contentious social, political and medical issue, it certainly will become so.

Critically impaired infants and end of life decision making : resource allocation and difficult decisions

Decisions to withdraw or withhold life-sustaining treatment are contentious, and offer difficult moral dilemmas to both medical practitioners and the judiciary. This issue is exacerbated when the patient is unable to exercise autonomy and is entirely dependent on the will of others.This book focuses on the legal and ethical complexities surrounding end of life decisions for critically impaired and extremely premature infants. Neera Bhatia explores decisions to withdraw or withhold life-sustaining treatment from critically impaired infants and addresses the controversial question, which lives are too expensive to treat? Bringing to bear such key issues as clinical guidance, public awareness, and resource allocation, the book provides a rational approach to end of life decision making, where decisions to withdraw or withhold treatment may trump other competing interests.The book will be of great interest and use to scholars and students of bioethics, medical law, and medical practitioners.

An integrated framework for suicide risk prediction

Suicide is a major concern in society. Despite of great attention paid by the community with very substantive medico-legal implications, there has been no satisfying method that can reliably predict the future attempted or completed suicide. We present an integrated machine learning framework to tackle this challenge. Our proposed framework consists of a novel feature extraction scheme, an embedded feature selection process, a set of risk classifiers and finally, a risk calibration procedure. For temporal feature extraction, we cast the patient’s clinical history into a temporal image to which a bank of one-side filters are applied. The responses are then partly transformed into mid-level features and then selected in 1-norm framework under the extreme value theory. A set of probabilistic ordinal risk classifiers are then applied to compute the risk probabilities and further re-rank the features. Finally, the predicted risks are calibrated. Together with our Australian partner, we perform comprehensive study on data collected for the mental health cohort, and the experiments validate that our proposed framework outperforms risk assessment instruments by medical practitioners.

Healthcare in continuum for an ageing population - national self monitoring or remote offshore monitoring for Australia?

Australia is a country, similar to other developed nations, confronting an ageing population with complex demographics. Ensuring continued healthcare for the ageing, while providing sufficient support for the already aged population requiring assistance, is at the forefront of the national agenda. Varied initiatives are with foci to leverage the advantages of ICTs leading to e-Health provisioning and assisted technologies. While these initiatives increasingly put budgetary constraints on local and federal governments, there is also a case for offshore resourcing of non-critical health services, to support, streamline and enhance the continuum of care, as the nation faces acute shortages of medical practitioners and nurses. However, privacy and confidentiality concerns in this context are a significant issue in Australia. In this paper, we take the position that if the National and state electronic health records system initiatives, are fully implemented, offshore resourcing can be a feasible complementary option resulting in a win-win situation of cutting costs and enabling the continuum of healthcare.

Hierarchical gene selection and genetic fuzzy system for cancer microarray data classification

This paper introduces a novel approach to gene selection based on a substantial modification of analytic hierarchy process (AHP). The modified AHP systematically integrates outcomes of individual filter methods to select the most informative genes for microarray classification. Five individual ranking methods including t-test, entropy, receiver operating characteristic (ROC) curve, Wilcoxon and signal to noise ratio are employed to rank genes. These ranked genes are then considered as inputs for the modified AHP. Additionally, a method that uses fuzzy standard additive model (FSAM) for cancer classification based on genes selected by AHP is also proposed in this paper. Traditional FSAM learning is a hybrid process comprising unsupervised structure learning and supervised parameter tuning. Genetic algorithm (GA) is incorporated in-between unsupervised and supervised training to optimize the number of fuzzy rules. The integration of GA enables FSAM to deal with the high-dimensional-low-sample nature of microarray data and thus enhance the efficiency of the classification. Experiments are carried out on numerous microarray datasets. Results demonstrate the performance dominance of the AHP-based gene selection against the single ranking methods. Furthermore, the combination of AHP-FSAM shows a great accuracy in microarray data classification compared to various competing classifiers. The proposed approach therefore is useful for medical practitioners and clinicians as a decision support system that can be implemented in the real medical practice.

Hidden Markov models for cancer classification using gene expression profiles

This paper introduces an approach to cancer classification through gene expression profiles by designing supervised learning hidden Markov models (HMMs). Gene expression of each tumor type is modelled by an HMM, which maximizes the likelihood of the data. Prominent discriminant genes are selected by a novel method based on a modification of the analytic hierarchy process (AHP). Unlike conventional AHP, the modified AHP allows to process quantitative factors that are ranking outcomes of individual gene selection methods including t-test, entropy, receiver operating characteristic curve, Wilcoxon test and signal to noise ratio. The modified AHP aggregates ranking results of individual gene selection methods to form stable and robust gene subsets. Experimental results demonstrate the performance dominance of the HMM approach against six comparable classifiers. Results also show that gene subsets generated by modified AHP lead to greater accuracy and stability compared to competing gene selection methods, i.e. information gain, symmetrical uncertainty, Bhattacharyya distance, and ReliefF. The modified AHP improves the classification performance not only of the HMM but also of all other classifiers. Accordingly, the proposed combination between the modified AHP and HMM is a powerful tool for cancer classification and useful as a real clinical decision support system for medical practitioners.

Disciplinary proceedings against doctors who abuse controlled substances

Study examined 27 reports from disciplinary tribunals against medical practitioners who abused narcotic analgesics (often combined with other drugs of addiction) between 2010 and 2015. The study covered all States and Territories except Tasmania (no reports were accessible for this jurisdiction. The reports revealed that 12 medical practitioners were in their 40s; five in their 30s; and one person still in the 20s. Although majority were General Practitioners (15 out of 27), other medical specialties were also represented. Self-administered Pethidine was the most prevalent opioid (11 out of 27), and was the only drug used alone. Morphine was self-administered by six doctors; the same number used high doses of Panadeine Forte, Codeine and Codeine Phosphate, and Fentanyl was abused by five doctors. Surprisingly, fewer medical practitioners appear to use such opiates such as Propofol, Tramadol and Tramol, Oxycodone and Endone. The examination of cases suggests lack of consistency in the imposition of professional sanctions and penalties by the relevant tribunals. The study concludes that disciplinary tribunals should apply the test of proportionality in the form of ‘reasonable necessity’ when deciding whether to remove or suspend the addicted medical practitioner from the Register.

Mass spectrometry cancer data classification using wavelets and genetic algorithm

This paper introduces a hybrid feature extraction method applied to mass spectrometry (MS) data for cancer classification. Haar wavelets are employed to transform MS data into orthogonal wavelet coefficients. The most prominent discriminant wavelets are then selected by genetic algorithm (GA) to form feature sets. The combination of wavelets and GA yields highly distinct feature sets that serve as inputs to classification algorithms. Experimental results show the robustness and significant dominance of the wavelet-GA against competitive methods. The proposed method therefore can be applied to cancer classification models that are useful as real clinical decision support systems for medical practitioners.

Cutting the cord: Can society over-invest in extremely premature and critically impaired neonates?

This article provides a critical examination of the allocation of scarce public health care funds in relation to extremely premature and sick neonates. Decisions to withdraw or withhold life-sustaining treatment from neonates born extremely premature are generally informed by arbitrary and often subjective considerations of those involved in their care – namely parents and medical practitioners. This article argues for a sharp and immediate focus in decisions to treat such neonates based on the allocation of limited health care resources. Accordingly, decisions to save and preserve the lives of imperilled neonates should not be limited to the immediate financial costs of medical treatment. More explicitly there should be a full appreciation of the cost of disability to the family, requirements for long-term care, and the benefits and associated costs of life, not only to the patient, but also to society.

Using Twitter to learn about the autism community

Considering the raising socio-economic burden of autism spectrum disorder (ASD), timely and evidence-driven public policy decision-making and communication of the latest guidelines pertaining to the treatment and management of the disorder is crucial. Yet evidence suggests that policy makers and medical practitioners do not always have a good understanding of the practices and relevant beliefs of ASD-afflicted individuals’ carers who often follow questionable recommendations and adopt advice poorly supported by scientific data. The key goal of the present work is to explore the idea that Twitter, as a highly popular platform for information exchange, could be used as a data-mining source to learn about the population affected by ASD—their behaviour, concerns, needs, etc. To this end, using a large data set of over 11 million harvested tweets as the basis for our investigation, we describe a series of experiments which examine a range of linguistic and semantic aspects of messages posted by individuals interested in ASD. Our findings, the first of their nature in the published scientific literature, strongly motivate additional research on this topic and present a methodological basis for further work.

Maus ares e malária: entre os pântanos de Natal e o feroz mosquito africano (1892-1932)

The development of epidemiological practices in the last years of the nineteenth and early twentieth century was characterized by both an influence of medical geography and the emergence of microbes and vectors of diseases. Both theories were used to explain outbreaks in Rio Grande do Norte specially in Natal. In this process were organized new institutions linked to public health, unhealthy spaces and prescribed hygiene measures. The redefinitions of the spaces were linked to updated elements of Hippocratic medicine such as aerism and emphasis on medical topography. How the physicians of the town were organized in the face of new meanings and fields of expertise in the demarcation of diseases and regulation of their own practices against the illegal medical practitioners? Likewise, the very occurrence of epidemics mobilized people, urban institutions and apparatuses. But how the Hippocratic legacy that leads to the idea of bad air originated by swamps from the eighteenth and nineteenth century has been linked to new microbial assumptions and disease vectors in the early twentieth century? How an invader from Africa, (the mosquito A. gambiae) mobilized transnational efforts to combat malaria and redefined the epidemiological practices? The aim of this work is to understand how epidemiological practices redefine the way we define spaces, practices and disease from both an approach influenced by a relational history of spaces and a theoretical synergy which includes topics in Science Studies, Post Structuralist Geography and some elements of Feminist Studies. Documentary research were surveyed in the reports of the provincial presidents, government posts to the Provincial Assembly, specialized medical articles and theses, and documents from the Rockefeller Foundation and national and international journals. In this regard shall be given to both material and discursive aspects of space-related practical epidemiological that Natal as much (in general) Rio Grande do Norte between bad air and malaria.

The viewpoint of health professionals on the role of basic units in the healthcare network of the Brazilian Unified Health System

In the process of creation of the Unified Health System (SUS) as a universal policy seeking to ensure comprehensive care, unscheduled assistance in primary healthcare units (UBS) is an unresolved challenge. The scope of this paper is to analyze the viewpoint of health professionals on the role of primary healthcare units in meeting this demand. It is a transversal study of qualitative data obtained through questionnaires and interviews with 106 medical practitioners from 6 emergency medical services and 190 professionals from 30 units. They explained why people seek emergency care for occurrences pertaining to primary care. The content analysis technique with thematic categories was used for data analysis. Lack of resources and problems with primary health unit work processes (50.8%) were the reasons most frequently cited by emergency care physicians to explain this inadequate demand. Only 33.3% of the health unit professionals agreed that these occurrences should be attended in the primary healthcare services. The limited viewpoint of the role of health services on the unscheduled care, particularly among primary care professionals, possibly leads to restrictive practices for access by the population.

Nursing Homes as a Setting for Psychological Practice: Public Policy Perspectives.

Recent federal legislation has provided renewed interest in improving the quality of nursing home care. The lack of both funding and personnel are significant barriers that may keep psychology's disciplinary expertise from being fully used in nursing homes. Nursing homes may be forced to undertake mandated activities (e.g., preadmission screening, nurses aides' training, and evaluation) without psychologists' expertise, relying either on medical practitioners with little knowledge of mental health interventions or on minimally qualified, entry-level mental health workers. Advocates for improved nursing home care must see the links among basic disciplinary skills, interdisciplinary collaboration, and improved care for mentally impaired elderly individuals.

Complementary and alternative medicine costs - a systematic literature review

Objective: The aim of this literature review, performed within the framework of the Swiss governmental Program of Evaluation of Complementary Medicine (PEK), was to investigate costs of complementary and alternative medicine (CAM). Materials and Methods: A systematic literature search was conducted in 11 electronic databases. All retrieved titles and reference lists were also hand-searched. Results: 38 publications were found: 23 on CAM of various definitions (medical and non-medical practitioners, over-the-counter products), 13 on homeopathy, 2 on phytotherapy. Studies investigated different kinds of costs (direct or indirect) and used different methods (prospective or retrospective questionnaires, data analyses, cost-effectiveness models). Most studies report 'out of pocket' costs, because CAM is usually not covered by health insurance. Costs per CAM-treatment / patient / month were AUD 7-66, CAD 250 and GBP 13.62 +/- 1.61. Costs per treatment were EUR 205 (range: 15-1,278), USD 414 +/- 269 and USD 1,127. In two analyses phytotherapy proved to be cost-effective. One study revealed a reduction of 1.5 days of absenteeism from work in the CAM group compared to conventionally treated patients. Another study, performed by a health insurance company reported a slight increase in direct costs for CAM. Costs for CAM covered by insurance companies amounted to approximately 0.2-0.5% of the total healthcare budget (Switzerland, 2003). Publications had several limitations, e.g. efficacy of therapies was rarely reported. As compared to conventional patients, CAM patients tend to cause lower costs. Conclusion: Results suggest lower costs for CAM than for conventional patients, but the limited methodological quality lowers the significance of the available data. Further well-designed studies and models are required.