Stephen William Hawking CH CBE FRS FRSA (8 January 1942 – 14 March 2018) was an English theoretical physicist, cosmologist, author and Director of Research at the Centre for Theoretical Cosmology within the University of Cambridge. His scientific works included a collaboration with Roger Penrose on gravitational singularity theorems in the framework of general relativity and the theoretical prediction that black holes emit radiation, often called Hawking radiation. Hawking was the first to set out a theory of cosmology explained by a union of the general theory of relativity and quantum mechanics. He was a vigorous supporter of the many-worlds interpretation of quantum mechanics. Hawking was an Honorary Fellow of the Royal Society of Arts (FRSA), a lifetime member of the Pontifical Academy of Sciences, and a recipient of the Presidential Medal of Freedom, the highest civilian award in the United States. In 2002, Hawking was ranked number 25 in the BBC's poll of the 100 Greatest Britons. He was the Lucasian Professor of Mathematics at the University of Cambridge between 1979 and 2009 and achieved commercial success with works of popular science in which he discusses his own theories and cosmology in general. His book, A Brief History of Time, appeared on the British Sunday Times best-seller list for a record-breaking 237 weeks. Hawking had a rare early-onset slow-progressing form of motor neurone disease (also known as amyotrophic lateral sclerosis and Lou Gehrig's disease), that gradually paralysed him over the decades. Even after the loss of his speech, he was still able to communicate through a speech-generating device, initially through use of a hand-held switch, and eventually by using a single cheek muscle. Hawking was born on 8 January 1942 in Oxford to Frank (1905–1986) and Isobel Hawking (née Walker; 1915–2013). Despite their families' financial constraints, both parents attended the University of Oxford, where Frank read medicine and Isobel read Philosophy, Politics and Economics. The two met shortly after the beginning of the Second World War at a medical research institute where Isobel was working as a secretary and Frank was working as a medical researcher. They lived in Highgate; but, as London was being bombed in those years, Isobel went to Oxford to give birth in greater safety. Hawking had two younger sisters, Philippa and Mary, and an adopted brother, Edward. In 1950, when Hawking's father became head of the division of parasitology at the National Institute for Medical Research, Hawking and his family moved to St Albans, Hertfordshire.
The University of Cambridge professor was an iconic figure in both the scientific community and in popular culture, known for his keen mind and humor, as well as his striking physical challenges. Dr. Hawking had long battled with amyotrophic lateral sclerosis, which left him wheelchair-bound for most of his life. Commonly known as Lou Gehrig's disease or motor neuron disease, the condition damages the nerves that control movement and results in paralysis. Patients with ALS typically die within five years of diagnosis. Dr. Hawking, who was diagnosed in 1963 at the age of 21, is believed to have been the longest-living survivor, a fact that still perplexes neurologists.
One of the most fundamental problems in causal inference is the estimation of a causal effect when variables are confounded. This is difficult in an observational study, because one has no direct evidence that all confounders have been adjusted for. We introduce a novel approach for estimating causal effects that exploits observational conditional independencies to suggest "weak" paths in a unknown causal graph. The widely used faithfulness condition of Spirtes et al. is relaxed to allow for varying degrees of "path cancellations" that imply conditional independencies but do not rule out the existence of confounding causal paths. The outcome is a posterior distribution over bounds on the average causal effect via a linear programming approach and Bayesian inference. We claim this approach should be used in regular practice along with other default tools in observational studies.
A company owned by Google has been given access to the healthcare data of up to 1.6 million patients from three hospitals run by a major London NHS Trust. DeepMind, the tech giant's London-based company most famous for its innovative use of artificial intelligence, is being provided with the patient information as part of an agreement with the Royal Free NHS trust, which runs the Barnet, Chase Farm and Royal Free hospitals. It includes information about people who are HIV-positive as well as details of drug overdoses, abortions and patient data from the last five years, according to a report by the New Scientist. DeepMind announced in February that it was developing a software in partnership with NHS hospitals to alert staff to patients at risk of deterioration and death through kidney failure. The technology, which is run through a smartphone app, has the support of Lord Darzi, a surgeon and former health minister who is director of the Institute of Global Health Innovation at Imperial College London.
Data-driven techniques for identifying disease subtypes using medical records can greatly benefit the management of patients' health and unravel the underpinnings of diseases. Clinical patient records are typically collected from disparate sources and result in high-dimensional data comprising of multiple likelihoods with noisy and missing values. Probabilistic methods capable of analysing large-scale patient records have a central role in biomedical research and are expected to become even more important when data-driven personalised medicine will be established in clinical practise. In this work we propose an unsupervised, generative model that can identify clustering among patients in a latent space while making use of all available data (i.e. in a heterogeneous data setting with noisy and missing values). We make use of the Gaussian process latent variable models (GPLVM) and deep neural networks to create a non-linear dimensionality reduction technique for heterogeneous data. The effectiveness of our model is demonstrated on clinical data of Parkinson's disease patients treated at the HUS Helsinki University Hospital. We demonstrate sub-groups from the heterogeneous patient data, evaluate the robustness of the findings, and interpret cluster characteristics.