Artificial intelligence in health care: enabling informed care

We read with interest the Lancet Editorial on artificial intelligence (AI) in health care (Dec 23, 2017, p 2739).1 Deep learning as a form of AI risks being overhyped. Deep neural networks contain multiple layers of nodes connected by adjustable weights. Learning occurs by adjusting these weights until the desired input-to-output function is achieved.2 With many millions of weights, huge amounts of data are required for learning, a process facilitated by recent increases in computational power. However, the learning algorithm, known as the error back-propagation algorithm, was invented in the 1980s and has been used to train neural networks ever since. Two decades ago, our neural network system scored sleep and diagnosed sleep disorders.3 Our machine learning algorithm,4, 5 which now provides early warning of deterioration in many hospitals, was commercialised a decade ago.6 A key change occurred in the early 2000s. Since then, error back-propagation learns features directly from the input data, rather than relying on expert-selected features (eg, microaneurysms for a neural network assessing diabetic retinopathy). The first layers become implicit feature detectors. The success of deep learning has been shown mainly in problems with inputs of image (or image-like) data, as shown in medical image analysis,7, 8 speech recognition, and board game playing. Deep learning also lacks explanatory power; deep neural networks cannot explain how a diagnosis is reached and the features enabling discrimination are not easily identifiable. Clinicians should be aware of the capabilities as well as current limitations of AI. Properly integrated AI will improve patient outcomes and health-care efficiency. Augmented intelligence at the point of care is likely to precede AI without human involvement. LT and PW are supported by the Biomedical Research Centre, Oxford. Both authors have received funding from the National Institute for Health Research. The authors have developed an electronic observations application for which Drayson Health has purchased a sole licence. Drayson Health has a research agreement with the University of Oxford and has paid LT personal fees for consultancy as a member of its Strategic Advisory Board. Drayson Health might pay PW consultancy fees in the future

Deep Learning-Enabled Sleep Staging From Vital Signs and Activity Measured Using a Near-Infrared Video Camera

Conventional sleep monitoring is time-consuming, expensive and uncomfortable, requiring a large number of contact sensors to be attached to the patient. Video data is commonly recorded as part of a sleep laboratory assessment. If accurate sleep staging could be achieved solely from video, this would overcome many of the problems of traditional methods. In this work we use heart rate, breathing rate and activity measures, all derived from a near-infrared video camera, to perform sleep stage classification. We use a deep transfer learning approach to overcome data scarcity, by using an existing contact-sensor dataset to learn effective representations from the heart and breathing rate time series. Using a dataset of 50 healthy volunteers, we achieve an accuracy of 73.4\% and a Cohen's kappa of 0.61 in four-class sleep stage classification, establishing a new state-of-the-art for video-based sleep staging.Comment: Accepted to the 6th International Workshop on Computer Vision for Physiological Measurement (CVPM) at CVPR 2023. 10 pages, 12 figures, 5 table

The Fast and the Spurious: Geographies of Youth Car Culture in Hamilton, New Zealand

“Boy racers” or “hoons” attract extensive media attention and are often the focus of public concern. Discourses about “hooning” often focus on notions of public safety and illegal behaviour. What is largely absent from these debates is alternative explanations as to why young people choose to engage in “hooning” behaviour, what drives them to congregate in public spaces and why they choose to express themselves through an “autocentric” culture. When these issues are addressed it is usually within broader policy frameworks which seek ways of dissipating youth activities in spaces constructed as “trouble spots”. This thesis represents an attempt to provide a reverse discourse about youth car culture and young people's presence in public spaces. Criminal activity not withstanding, youth car culture behaviour in this context is treated as a legitimate form of cultural expression that has the same social validity as other non-mainstream phenomena. Through feminist and poststructuralist understandings of identities, landscapes and place, the complexities of youth car culture will be unpacked in an attempt to expose “concerns” which may turn out to be little more than moral panic

Continuous Physiological Monitoring of Ambulatory Patients

A poster originally presented at the "MEC Annual Meeting and Bioengineering14" conference (Imperial College London, 8th - 9th September 2014)

Heteroleptic [Cu(P^P)(N^N)][PF6] compounds with isomeric dibromo-1,10-phenanthroline ligands

A series of [Cu(P^P)(N^N)][PF6] compounds are reported in which N^N is 2,9-dibromo-1,10-phenanthroline (2,9-Br2phen), 3,8-dibromo-1,10-phenanthroline (3,8-Br2phen) or 4,7-dibromo-1,10-phenanthroline (4,7-Br2phen) and P^P is bis(2-(diphenylphosphano)phenyl)ether(POP) or 4,5-bis(diphenylphosphano)-9,9-dimethylxanthene(xantphos). The compounds have been characterized by solution multinuclear NMR spectroscopy, mass spectrometry and single-crystal X-ray analysis. Each compound undergoes a partially-reversible or irreversible copper-centred oxidation, the highest potential being for 2,9-Br2phen-containing compounds. In solution, the compounds are weak yellow or orange emitters, whereas powdered samples exhibit yellow emissions with photoluminescence quantum yields of up to 45% for [Cu(xantphos)(2,9-Br2phen)][PF6] with an excited state lifetime τ1/2= 9.9 μs. Values of λemmaxfor [Cu(POP)(2,9-Br2phen)][PF6] and [Cu(xantphos)(2,9-Br2phen)][PF6] are blue-shifted with respect to compounds with the 3,8- and 4,7-isomers, both in solution and the solid stat