Examples include systems that automatically replenish inventory based on weather patterns and historical trends, or that optimize truck routes using Google API data on traffic. That means moving the understanding of data--including predictive analytics--from executive dashboards to core business processes. This data is analyzed against historical patterns for thousands of other patients and include outcomes of those data patterns, such as a heart attack or stroke. In this case, the machine learning system takes only three data sets into account: traffic, weather, and route data.
Richard Dabate told police a masked intruder assaulted him and killed his wife in their Connecticut home. Detectives suspected foul play and obtained data from Bates's Amazon Echo device. Smart cars, fridges, doorbells, watches, phones, Fitbits, sneakers, televisions, gaming consoles, coffee makers, Pacemakers – a fast proliferating list – all can monitor, record and be used as evidence. "I think everyone realises – good guys, bad guys, cops, robbers – that everything is being videotaped or tracked somehow," Andy Kleinick, the head of the Los Angeles police department's cyber crimes section, and a supervisor for the secret service's LA electronic crimes task force, said in an interview.
"We intend to incorporate the clinical data routinely acquired with each scan and begin to predict other medical outcomes, like the development of strokes and heart attacks," Palmer tells ResearchGate. They hope that aside from predicting life span, this could be used to detect diseases early on. "We want to one day use this technology to predict the onset of chronic diseases such as diabetes, heart disease, and cancer before any symptoms are evident," says Palmer. In chronic conditions like cancer, early detection and treatment is the biggest factor between life and death.
The plan was to have her watch her weight daily, and every time there was any sign of increased fluid, to recommend an extra dose -- an extra pill -- of Lasix, to restore her fluid balance. On the other hand, a friend's father, a physician, had defied the odds and lived for decades with severe heart failure by weighing himself daily and adjusting his medications accordingly. If her weight increases by more than 1 pound in one day, recommend one extra Lasix pill. If the weight does not return to normal by the second day after the extra dose, then give one additional dose the third day.
The Institute of Medicine (now the National Academy of Medicine) says clinical practice guidelines should be based on a systematic review of the evidence, lead author Dr. Paul Shekelle from RAND Corporation in Santa Monica, California, told Reuters Health by email. In all three cases, computers - provided only with the titles and summaries of articles included in previous reviews - reduced the number of articles researchers had to screen further by 67 to 83 percent, according to the results in Annals of Internal Medicine. "Machine learning methods are very promising as a way to reduce the amount of time and effort for the literature search, which in turn should make it easier to update the systematic review, which in turn can facilitate keeping clinical practice guidelines up to date," Shekelle said. "The critical step is training properly the computer systems - we need to ensure research dollars are provided to ensure this training is done by serious and independent researchers and controlled by public institutions."
For the study, five year–old medical images of 48 patient's chests were analyzed by artificial intelligence. Oakden–Rayner added that previous research using clincial data such as age, sex or physical fitness had between 65 percent and 75 percent accuracy, so the new study "compare[s] favorably, especially considering we excluded factors like age and sex from our analysis." To predict mortality at ten years, for example, the system would need to analyze CT scans performed over ten years ago so that Oakden–Rayner's team could have the follow up results. Similar medical AI news has been cropping up lately: a startup in China revealed an AI system that can help doctors identify lung cancer by examining CT scans, and IBM now has AI in hospitals (called Watson) that can answers patient questions.
Recently, a clinical study led by Finnish researchers was launched in Tampere to use the latest analytical methods to recognize those myocardial infarction patients at high risk of complications. The project is being implemented as a collaboration between the University of Tampere, VTT, Polytechnic University of Milan (Politecnico di Milano), the TAYS Heart Hospital, and the industrial partners GE Healthcare Finland Oy, Bittium Corporation, Clothing Oy and Fimlab Oy. The project is funded by Tekes' Bits of Health programme, the University of Tampere, VTT, GE Healthcare Finland Oy, Bittium Corporation and Fimlab Oy. About VTT Technical Research Centre of Finland VTT Technical Research Centre of Finland Ltd is the leading research and technology company in the Nordic countries.
Their models suggested that a drone would arrive faster than an ambulance 93% of the time, saving patients an average of 19 minutes. Now they've gone a step further and dispatched an actual drone from a fire station about 45 minutes north of Stockholm to 18 locations where people suffered actual cardiac arrests away from a hospital between 2006 and 2014. The time it took to get the drone dispatched, launched and to the site of a cardiac arrest ranged from a low of 1 minute, 15 seconds to a high of 11 minutes, 51 seconds. For the drones, the median time from dispatch to arrival was 5 minutes, 21 seconds.
Scientists, data scientists that is, from the University of Adelaide in Australia have announced that they have managed to build an Artificial Intelligence (AI) that can predict when people are going to die, and it's 70 percent accurate, but unlike the AI's I've talked about before that can predict how long people who have had heart attacks have left to live, more accurately than human doctors, this one is different – it can predict when you're going to die irrespective of the state of your current health because it uses deep learning to analyse a range of different scans, such as CT scans, to search for the signs, and assess the severity of, heart disease, cancer, and other diseases. See, in one fell swoop you've assessed the state of your overall health, roughly assessed the risk factors in your head and calculated the rough odds of how long you think you have left to live. They used a dataset of historical CT scans, and excluding other predictive factors like age, the system was able to predict whether patients would die within five years with a 70 percent accuracy rate. "The goal of the research isn't really to predict death, but to produce a more accurate measurement of health," said Dr. Luke Oakden-Rayner, a researcher on the project, "a patient's risk of death is directly related to the health of their organs and tissues, but the physiological changes associated with chronic diseases often build up for decades before we see the final, sometimes fatal, symptoms.
Just recently s group of researchers forms the University of Nottingham, U.K. developed a machine learning algorithm that had the ability to predict a patient's chances of having a heart attack or stroke. Had Watson not have asked about pets as part of the patient intake form, doctor's may not have diagnosed the patient until much later, allowing time for worse symptoms to occur. Some of the world's largest companies and most recognized universities are turning to AI and machine learning to boost productivity. At the other end of the world, Oxford University researchers have developed a lipreading AI called LipNet (pdf) that is around 10 times more accurate than your average lipreader.