Do you want to know when you will die? Google is training its new artificial intelligence computer to predict whether hospital patients will die 24-hours after admission. The accuracy of the initial trials is at 95%. But how could it know that? The system uses basic data such as a patient’s age, ethnicity and gender. A new algorithm then uses information like patient’s vitals, medical history and even PDF’s, charts, and hand-written notes from doctors to make its predictions. The technology can also predict the number of days a patient will be in the hospital or the chances of being readmitted.<doc-sep>Google’s reach into almost all aspects of people’s digital lives – from email and search, to maps and calendars – means there isn’t much it doesn’t know about its 1.17 billion users. And now, the technology giant might even know when you’re going to die. The company’s Medical Brain team is using a new type of artificial intelligence algorithm to make predictions about the likelihood of death among hospital patients. A paper published last month in the journal Nature detailed how its predictive algorithm used vast data sets to determine whether inpatients will survive in two different hospitals. ✕ What is Project Maven? : the Pentagon's AI project with Google For predicting inpatient mortality, Google’s Medical Brain was 95 per cent accurate in the first hospital and 93 per cent accurate in the second hospital. “This was significantly more accurate than the traditional predictive model,” the paper stated. “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.” In one case study, the Medical Brain algorithm gave a woman with metastatic breast cancer a 19.9 per cent chance of dying in the hospital by crunching 175,639 data points from her medical records. The hospital's Early Warning Score that it typically uses gave her a 9.3 per cent chance of dying. Within two weeks the patient was dead. Gadget and tech news: In pictures Show all 25 1 /25 Gadget and tech news: In pictures Gadget and tech news: In pictures Gun-toting humanoid robot sent into space Russia has launched a humanoid robot into space on a rocket bound for the International Space Station (ISS). The robot Fedor will spend 10 days aboard the ISS practising skills such as using tools to fix issues onboard. Russia's deputy prime minister Dmitry Rogozin has previously shared videos of Fedor handling and shooting guns at a firing range with deadly accuracy. 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It has already courted controversy, however, after it was revealed in 2013 that DeepMind had access to 1.6 million medical records of NHS patients at three hospitals run by London's Royal Free Trust. A leaked letter from Dame Fiona Caldicott, the head of the Department of Health's National Data Guardian, questioned the access deal by suggesting it may be have been made on an "inappropriate legal basis."<doc-sep>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>
You can Google just about any piece of information these days, and that could soon include when you'll kick the bucket. The tech giant has partnered up with Stanford University to help test a new computer system to predict the time-of-death of hospital patients. Using artificial intelligence, the techy types say the accuracy of their program is as much as 95%. It works by taking in personal data such as age and ethnicity which is then combined with hospital data like vital signs and any prior diagnoses. As the AI program is used more and more, it gets smarter at predicting the death of the patient. (Image: Getty) "These models outperformed traditional, clinically-used predictive models in all cases," explained Google's Alvin Rajkomar. The was trained through analysis of 160,000 adult and child patient files from Stanford and Lucile Packard Children's hospital. When the algorithm was applied to 40,000 active patients (it was asked to predict which would die in the next three to twelve months) it was correct in 90% of cases. "The scale of data available allowed us to build an all-cause mortality prediction model, instead of being disease or demographic specific," said Anand Avati, a member of Stanford University's AI Lab, reported IBTimes . Kenneth Jung, a research scientist at Stanford University said: "We think that keeping a doctor in the loop and thinking of this as 'machine learning plus the doctor' is the way to go as opposed to blindly doing medical interventions based on algorithms... that puts us on firmer ground both ethically and safety-wise".