Securing medical research data with a rights management system

We propose a digital rights management approach for sharing electronic health records for research purposes and argue advantages of the approach. We give an outline of our implementation, discuss challenges that we faced and future directions.

Dragline automation - a decade of development : shared autonomy for improving mining equipment productivity

Draglines are massive machines commonly used in surface mining to strip overburden, revealing the targeted minerals for extraction. Automating some or all of the phases of operation of these machines offers the potential for significant productivity and maintenance benefits. The mining industry has a history of slow uptake of automation systems due to the challenges contained in the harsh, complex, three-dimensional (3D), dynamically changing mine operating environment. Robotics as a discipline is finally starting to gain acceptance as a technology with the potential to assist mining operations. This article examines the evolution of robotic technologies applied to draglines in the form of machine embedded intelligent systems. Results from this work include a production trial in which 250,000 tons of material was moved autonomously, experiments demonstrating steps towards full autonomy, and teleexcavation experiments in which a dragline in Australia was tasked by an operator in the United States.

Using digital rights management for securing data in a medical research environment

We propose a digital rights management approach for sharing electronic health records in a health research facility and argue advantages of the approach. We also give an outline of the system under development and our implementation of the security features and discuss challenges that we faced and future directions.

Children and consent to medical treatment

This chapter deals with the law concerning children and consent to medical treatment. Where a child under the age of 18 requires medical treatment, issues arise as to who may lawfully consent to the treatment and under what circumstances. Depending on the circumstances, consent may be given by the child’s parent or guardian; the child; or a court. The chapter provides a thorough treatment of Australian law about these issues and circumstances.

Finding a way through the ethical and legal maze : withdrawing medical treatment and euthanasia

This article examines Finnis' and Keown's claim that the intention/foresight distinction should be used as the basis for the lawfulness of withholding and withdrawing medical treatment, rather than the act/omission distinction which is currently used. I argue that whilst the intention/foresight distinction is sound and can apply to palliative pain relief hastening death, it cannot be applied to withholding and withdrawing medical treatment. Instead, the act/omission distinction remains the better basis for the lawfulness of withholding and withdrawal, and law reform is consequently unnecessary.

Recent developments : loss of chance in medical litigation : Tabet v Gett [2010] HCA 12.

Following the judgement of the High Court in Tabet v Gett [2010]HCA 12 handed down on 21 April 2010 it appears that in Australia there is now very limited scope for recovery in negligence for the loss of a chance of a better medical outcome.

Analysis of the effect of negation on information retrieval of medical data

Most information retrieval (IR) models treat the presence of a term within a document as an indication that the document is somehow "about" that term, they do not take into account when a term might be explicitly negated. Medical data, by its nature, contains a high frequency of negated terms - e.g. "review of systems showed no chest pain or shortness of breath". This papers presents a study of the effects of negation on information retrieval. We present a number of experiments to determine whether negation has a significant negative affect on IR performance and whether language models that take negation into account might improve performance. We use a collection of real medical records as our test corpus. Our findings are that negation has some affect on system performance, but this will likely be confined to domains such as medical data where negation is prevalent.