Multi-task learning (MTL) is a machine learning technique aiming to improve model performance by leveraging information across many tasks. It has been used extensively on various data modalities, including electronic health record (EHR) data. However, despite significant use on EHR data, there has been little systematic investigation of the utility of MTL across the diverse set of possible tasks and training schemes of interest in healthcare. In this work, we examine MTL across a battery of tasks on EHR time-series data. We find that while MTL does suffer from common negative transfer, we can realize significant gains via MTL pre-training combined with single-task fine-tuning. We demonstrate that these gains can be achieved in a task-independent manner and offer not only minor improvements under traditional learning, but also notable gains in a few-shot learning context, thereby suggesting this could be a scalable vehicle to offer improved performance in important healthcare contexts.
Robert Wachter, a former member of Google's healthcare advisory board, remembers when the company first set its sights on the healthcare industry more than a decade ago. "They said: We're Google, we'll solve it," says Wachter, head of medicine at University of California, San Francisco. At the time, Google was trying to create individual accounts where users could store their electronic medical records. So when then-chief executive Eric Schmidt later abandoned the effort with an admission that Google had underestimated the challenge, it came as a shock. "They conquer industry after industry, it doesn't seem like this would be very different," Wachter says.
Cerner was interviewing Silicon Valley giants to pick a storage provider for 250 million health records, one of the largest collections of U.S. patient data. Google dispatched former chief executive Eric Schmidt to personally pitch Cerner over several phone calls and offered around $250 million in discounts and incentives, people familiar with the matter say. Google had a bigger goal in pushing for the deal than dollars and cents: a way to expand its effort to collect, analyze and aggregate health data on millions of Americans. Google representatives were vague in answering questions about how Cerner's data would be used, making the health-care company's executives wary, the people say. Eventually, Cerner struck a storage deal with Amazon.com The failed Cerner deal reveals an emerging challenge to Google's move into health care: gaining the trust of health care partners and the public.
Its impact is drastic and real: Youtube's AIdriven recommendation system would present sports videos for days if one happens to watch a live baseball game on the platform ; email writing becomes much faster with machine learning (ML) based auto-completion ; many businesses have adopted natural language processing based chatbots as part of their customer services . AI has also greatly advanced human capabilities in complex decision-making processes ranging from determining how to allocate security resources to protect airports  to games such as poker  and Go . All such tangible and stunning progress suggests that an "AI summer" is happening. As some put it, "AI is the new electricity" . Meanwhile, in the past decade, an emerging theme in the AI research community is the so-called "AI for social good" (AI4SG): researchers aim at developing AI methods and tools to address problems at the societal level and improve the wellbeing of the society.
A former patient of the University of Chicago Medical Center is suing the institution amid claims it violated patients' privacy rights. The class-action lawsuit claims records containing identifiable patient information were shared as a result of a partnership between Google and the University of Chicago. All three institutions are named as defendants in the suit, which was filed Wednesday in the Northern District of Illinois by Matt Dinerstein, who received treatment at the medical center during two hospital stays in 2015. The collaboration between Google and the University of Chicago was launched in 2017 to study electronic health records and develop new machine-learning techniques to create predictive models that could prevent unplanned hospital readmissions, avoid costly complications and save lives, according to a 2017 news release from the university. The tech giant has similar partnerships with Stanford University and the University of California-San Francisco.
Using raw data from the entirety of a patient's electronic health record, Google researchers have developed an artificial intelligence network capable of predicting the course of their disease and risk of death during a hospital stay, with much more accuracy than previous methods. The deep learning models were trained on over 216,000 deidentified EHRs from more than 114,000 adult patients, who had been hospitalized for at least one day at either the University of California, San Francisco or the University of Chicago. For those two academic medical centers, the AI predicted the risks of mortality, readmission and prolonged stays, as well as discharge diagnoses, by ICD-9 code. The network was 95% accurate in predicting a patient's risk of dying while in the hospital--with a much lower rate of false alerts--than the traditional regressive model--the augmented Early Warning Score--which measures 28 factors and was about 85% accurate at the two centers. The researchers' findings were published last month in the Nature journal npj Digital Medicine.
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.
Google study claims algorithm is 95 percent accurate when predicting when a patient is going to die within 24 hours of being admitted to the hospital. Google has developed an artificial intelligence algorithm that could predict when you'll die with up to 95 percent accuracy, according to the tech giant's researchers. The research, which tackles a range of clinical issues among hospital patients, was recently published in the journal Nature. Google applied artificial intelligence to a vast amount of data from more than 216,000 adult patients hospitalized for at least 24 hours each in two medical centers. The research tapped into data from Electronic Health Records.
SAN FRANCISCO--(BUSINESS WIRE)--The following is an opinion editorial provided by Navin Shenoy, executive vice president and general manager of the Data Center Group at Intel Corporation. In the wide world of big data, artificial intelligence (AI) holds transformational promise. Everything from manufacturing to transportation to retail to education will be improved through its application. But nowhere is that potential more profound than in healthcare, where every one of us has a stake. What if we could predict the next big disease epidemic, and stop it before it kills?
This article first appeared in the September 2017 issue of HealthLeaders magazine. Promises and prognostications about the potential of artificial intelligence are being made in Silicon Valley, the Boston area, and other high-tech hot spots. Most hospitals and other clinical sites, however, do not exist in these cutting-edge environs. For many of the clinicians and hospital executives in this country who are busy enough grappling with a complex mix of challenges in a changing industry, AI in healthcare represents the latest technological buzzword, the hyped-up, futuristic stuff of drawing boards and tech magazines. The skepticism toward another technological innovation is understandable in an industry that is still struggling to identify an exact return on investment for the massive spending on the electronic health records mandate over the past decade.