GOOGLE knows everything (or at least it feels that way), and now it can even tell you when you'll die. The tech giant helped test an artificial intelligence computer system that can predict whether hospital patients will pass away 24 hours after admission. 2 Google used machine learning to predict the death of hospital patients Credit: Shutterstock What's more staggering is that trials put the accuracy of the AI's predictions as high as 95%. It works by chewing up data about patients, like their age, ethnicity and gender. This information is then joined up with hospital information, like prior diagnoses, current vital signs, and any lab results. And what makes the system particularly accurate is that it's fed data typically out of reach for machines, like doctors notes buried away on charts or in PDFs. 2 Google says hospitals could use its death prediction tech to improve patient care Credit: Getty - Contributor Demonstration of how Google Assistant will be able to make actual phone calls for you Artificial intelligence systems become smarter over time through a process known as machine learning. The AI was developed by a team of researchers from Stanford, the University of Chicago, and UC San Francisco. Google then took the AI system and "taught" it using de-identified data of 216,221 adults from two US medical centres. This meant the AI had more than 46billion data points to hoover up. Over time, the AI was able to associate certain words with an outcome (i.e. life, or death), and understand how likely (or unlikely) someone was to die. What's particularly exciting about Google's system is that researchers can throw almost any type of data at it. Stanford professor Nigam Shah told Bloomberg that around 80% of development time spent on predictive models goes on making the data look presentable for the AI. But Google's system can chew up anything and make predictions based on it, due to its powerful machine learning capabilities. Excitingly, Google's AI doesn't just predict whether you'll live or die. It can also guess at the length of a patient's stay in hospital, and their chances of being readmitted. MOST READ IN TECH BOUNCE BACK Dogecoin founder thanks Elon Musk for tweet as crypto market stages comeback Live Blog LOOK UP Lunar eclipse 2021 LIVE: Blood Moon and total eclipse happening NOW PHENOMENON Lunar eclipse 2021: Why the Super Blood Moon isn't visible in the UK LOST & FOUND New Google Maps feature means you'll NEVER get lost again OPEN WINDOW Microsoft boss reveals NEW 'next-gen' Windows coming six years after Windows 10 BLOOMING Super Flower Moon will fill skies tonight – how to see stunning lunar spectacle So exactly how accurate is the AI? When we talk about probability, a 1.00 score is perfectly accurate. And a 0.50 score is a 50/50 chance – so an AI that scores 0.50 is no better than a human making random guesses. Here's how Google's AI fared based on various outcomes: Predicting if a patient would stay long in a hospital – 0.86 (Google) vs 0.76 (traditional methods) Predicting inpatient mortality – 0.95 (Google) vs 0.86 (traditional methods) Predicting unexpected readmissions after a patient was discharged – 0.77 (Google) vs 0.70 (traditional methods) "These models outperformed traditional, clinically-used predictive models in all cases," explained Google's Alvin Rajkomar. Rajkomar said that hospitals adopting the AI could be use it to "improve care" for patients. Do you think artificial intelligence is a force for good? Let us know in the comments! We pay for your stories! Do you have a story for The Sun Online news team? Email us at tips@the-sun.co.uk or call 0207 782 4368 . We pay for videos too. Click here to upload yours.<doc-sep>Google has seeped into our lives and it is no surprise that it knows a lot about us. From our contacts to our address, there is hardly anything hidden from Google and now it can even predict the survival chances of a hospital patient. Google's Medical Brain Team has integrated artificial intelligence to predict the death among hospital patients. A paper published in The Nature last month details on how Google used a set of algorithm to predict the death of patients in two different hospitals. It was observed that Google was 95 per cent accurate in its prediction for the first hospital and 93 per cent accurate in the second. Google has created a tool that can predict the patient's survival chances in the hospital, their chances of re-admission and how long the patient could stay in the hospital. Bloomberg reported the case of a woman with late-stage breast cancer who saw two doctors and got a radiology scan. The doctors predicted 9.3 per cent chance of her dying during the stay. Google AI predicted that the risks of death were 19.9 per cent higher. The lady passed away in few days. According to the report, Google AI uses neural networks, a type of AI software which is good at automatically learning and improving itself. What impressed the researchers was the AI's ability to go through all the previous files and reports that may have been a little difficult to access for any human. The AI went all through all the past health records and data to give almost 95 per cent accurate predictions. Also, the prediction was faster and more accurate than the existing technologies. According to the report, Google aims to move this technology to hospitals and clinics in future. The Medical Brain team is also working on an AI system that can predict symptoms and disease with a good level of accuracy. ALSO READ: Satya Nadella and Microsoft employees fire off emails to each other over ICE project AI researchers at Google have also developed algorithms that can predict the risk of heart attacks by analysing retina scans. Google Brain has also collaborated with doctors in India and the US to help diagnose Diabetes Retinopathy. It is a medical condition in which retina is damaged due to diabetes.<doc-sep>Google puts AI team's work to good use in Android P Watch Now Video: Google puts AI team's work to good use in Android P. Having been trained on 46 billion bits of electronic health data from patients, Google's AI is now showing promise in the field of predicting health outcomes for patients. Researchers from Google Brain and Stanford University recently published a paper in Nature detailing their work using big data and deep-learning methods to predict the fate of inpatients. The researchers used the algorithms to predict important outcomes, such as death; readmissions to measure quality of care; a patient's length of stay to measure of resource utilization; and a prediction of a patient's diagnoses to see how well clinicians understood a patient's problems. The team took a different approach to building predictive statistical models by considering a 'representation' of all a patient's health records, including clinical notes, rather than removing most of a patient's information from the analysis. As noted, 80 percent of the effort in creating an analytic model is in cleaning the data, so it could provide a way to scale up predictive models, assuming the data is available to mine. They also developed a way to show clinicians what exact data its model "looked at" for each patient it predicted an outcome for. This technique would allow clinicians to check whether a prediction is based on credible facts and address concerns about so-called 'black-box' methods that don't explain why a prediction has been made. Google started working on the project with UC San Francisco, the University of Chicago Medicine, and Stanford Medicine last year, which gave them access to a vast trove of de-identified medical records to validate there deep-learning models. In total, they had access to health records on 216,221 adult patients who were hospitalized for 24 hours or more, which produced over 46 billion data points. "We demonstrate that deep-learning methods using this representation are capable of accurately predicting multiple medical events from multiple centers without site-specific data harmonization," the researchers note. As Bloomberg reports, medical experts have been impressed by Google's ability to dig out data from notes on PDFs or handwritten notes on old charts, which previously have been difficult to incorporate into predictive models. Google's system is both faster and more accurate than previous techniques. The study has created excitement at Google because it may open a new door to the lucrative healthcare market, where it could one day sell AI-as-a-service to time-constrained clinicians. The research showed that Google's models are better at predicting a range of outcomes and metrics for patients than traditional methods. On inpatient mortality, for example, it scored 0.95 out of a perfect score of 1.0 compared with traditional methods, which scored 0.86. In a blogpost, Google downplayed the idea that its AI would replace human clinicians' role in diagnosing patients. "We emphasize that the model is not diagnosing patients -- it picks up signals about the patient, their treatments and notes written by their clinicians, so the model is more like a good listener than a master diagnostician," the researchers note. Previous and related coverage Google Research becomes Google AI to reflect AI-first ambitions Google Research gets a rebrand to become Google AI. The message: Everything Google does intersects with AI somehow. Google employee protest: Now Google backs off Pentagon drone AI project Google won't bid to renew its Project Maven contract with the Pentagon after it expires in 2019. Google aims to build diversity in AI with new educational tools The site "Learn with Google AI" features a free course on machine learning that's available to anyone. Google urges 'fair and responsible' AI development Aside from ensuring access to machine learning expertise, the tech giant says there needs to be "fair and responsible" development of artificial intelligence to ensure societies can truly benefit from the technology.<doc-sep>
How much information can Google get from your phone? How much information can Google get from your phone?... GOOGLE is getting into healthcare and is touting the power of its artificial intelligence systems to determine the likely fate of patients. With the enough data, the company thinks it can predict when a patient will die with up to 95 per cent accuracy. In May, Google scientists published the account of a woman who came to hospital with late stage breast cancer and fluid building in her lungs. After the hospital equipment and computers took the woman’s vital signs, it estimated that she had a 9.3 per cent chance of dying during her stay at the hospital. Then it was Google’s turn. Its neural network, a type of artificial intelligence that can analyse huge reams of data and automatically learn and improve, was fed 175,639 data points on the woman including past health records and her current vital signs. It delivered a decidedly more grim prediction, calculating the woman had a 19.9 per cent chance of dying during her stay. She reportedly passed away a couple days later. That heartbreaking anecdote was reported by Bloomberg this week after it was published in May by Google-backed researchers in the scientific journal Nature. The initial findings of the proof-of-concept study suggest that Google’s system is faster, and more accurate than other techniques at evaluating a patient’s medical history and forecasting a range of patient outcomes such as mortality, hospital re-admission, prolonged hospital stay and discharge diagnosis. “We were interested in understanding whether deep learning could produce valid predictions across a wide range of clinical problems and outcomes,” the researchers wrote. “These models outperformed traditional, clinically used predictive models in all cases. We believe that this approach can be used to create accurate and scalable predictions for a variety of clinical scenarios.” Where the system was able to add value was its ability to incorporate data not easily analysed by traditional systems such as clinical notes buried in PDFs or scribbled on old charts, while disregarding redundant data. In total, Google has analysed 216,221 hospitalisations and 114,003 patients, which comes to more than 46 billion data points. The proof-of-concept study found that the data-crunching algorithm could accurately predict risk of mortality, hospital re-admission, prolonged hospital stay and discharge diagnosis. As hospitals work to improve electronic health records of patients, Google’s tools could be hugely useful. According to the research, the algorithm was 95 per cent accurate at predicting patient mortality based on data from the University of California San Francisco (UCSF) health system and 93 per cent accurate using data from the University of Chicago Medicine system. Google, or more accurately its parent company Alphabet, is arguably the world leader in developing systems of artificial intelligence. The emerging technology is tipped to reshape our world in unimaginable ways. It’s application to healthcare is already well and truly underway with self learning algorithms being used to detect things like skin cancer with greater accuracy than humans could hope to achieve. But the data that underpins the neural network described in the journal paper does raise some questions if this technology is to be adopted. Speaking to Fox News, Dr Mikhail Varshavski raised some of the concerns around privacy and the quality of data. “The thing that is worrying for me is what happens with this data and who owns this data?” he said. “I hope, as a doctor, that these companies use the data to benefit the patients, not the companies themselves.” Dr Varshavski also pointed out the huge risk in relying on predictions or assessments if the data used by the AI computer isn’t as reliable as we think. “Machines make mistakes and sometimes they make mistakes based on faulty data,” Dr Varshavski added. “There needs to be oversight of what these things do.”