You are free to share this article under the Attribution 4.0 International license. A new method that uses deep learning to analyze vast amounts of personal health record data could identify early signs of heart failure, researchers say. A paper, which appears in the Journal of the American Medical Informatics Association (JAMIA), describes how the method addresses temporality in the data--something previously ignored by conventional machine learning models in health care applications. The research uses a deep learning model to allow earlier detection of the incidents and stages that often lead to heart failure within 6-18 months. To achieve this, researchers use a recurrent neural network (RNN) to model temporal relations among events in electronic health records.
Artificial intelligence is a fascinating but not particularly accessible technology. Project DataREACH, currently in trial mode in Cameroon, wants to change that by giving doctors in all corners of the globe access to advanced AI to help diagnose illnesses and spot troubling health trends. "Our goal is to bridge the gap between the medical data now being gathered on the ground in developing nations, and the cutting-edge [AI] research and application...from the West," Project DataREACH founder and CEO Vikash Singh told PCMag. Singh's app allows clinicians to collect patient data like height, weight, blood pressure, cholesterol, family history, and location. That data is then analyzed via machine learning to assist physicians in evaluating the risk of noncommunicable diseases, including diabetes and cardiovascular issues.
Artificial intelligence originally aspired to replace doctors. Researchers imagined robots that could ask you questions, run the answers through an algorithm that would learn with experience and tell whether you had the flu or a cold. However, those promises largely failed, as artificial intelligent algorithms were too rudimentary to perform those functions. Particularly tricky was the variability between people, which caused basic machine learning algorithms to miss the patterns. Eventually though, a subset of AI called deep learning became sensitive enough to recognize speech from voice data.
AI diagnostic tools can find problems including retinal disease but they need to be developed with care.Credit: Lester V. Bergman/Getty One of the biggest -- and most lucrative -- applications of artificial intelligence (AI) is in health care. And the capacity of AI to diagnose or predict disease risk is developing rapidly. In recent weeks, researchers have unveiled AI models that scan retinal images to predict eye- and cardiovascular-disease risk, and that analyse mammograms to detect breast cancer. Some AI tools have already found their way into clinical practice. AI diagnostics have the potential to improve the delivery and effectiveness of health care.
Microsoft today said it will expand its footprint in India's healthcare sector by bringing in artificial intelligence for cardiology. "One of the things you immediately find out is that there's tremendous possibilities in extracting insights from how health data lead to better outcomes in what doctors do," Dr. Peter Lee, corporate vice president, AI & Research, at Microsoft told PTI after he announced the initiative in Las Vegas on the sidelines of the Healthcare Information and Management Systems Society (HIMSS) annual conference and exhibition. Microsoft has partnered with Apollo Hospital to create an AI-focused network in cardiology. It has also announced the expansion of the Microsoft Intelligent Network for eyecare. The Apollo partnership, he said, will work to develop and deploy new machine learning models to predict patient risk for heart disease and assists doctors on treatment plans.
New research findings suggest that machine learning may usher in a new era in digital healthcare tools that are able to predict clinical outcomes in patients with known or potential heart problems. These findings are from several studies being presented at the American College of Cardiology's 67th Annual Scientific Session.The new studies presented at ACC.18 demonstrate how machine learning can be used to predict outcomes such as diagnosis, death or hospital readmission; improve upon standard risk assessment tools; elucidate factors that contribute to disease progression; or to advance personalised medicine by predicting a patient's response to treatment.
The computer will see you now. Artificial intelligence algorithms may soon bring the diagnostic know-how of an eye doctor to primary care offices and walk-in clinics, speeding up the detection of health problems and the start of treatment, especially in areas where specialized doctors are scarce. The first such program -- trained to spot symptoms of diabetes-related vision loss in eye images -- is pending approval by the U.S. Food and Drug Administration. While other already approved AI programs help doctors examine medical images, there's "not a specialist looking over the shoulder of [this] algorithm," says Michael Abràmoff, who founded and heads a company that developed the system under FDA review, dubbed IDx-DR. "It makes the clinical decision on its own."
Emergency dispatchers have a tough job, trying to handle as many calls as they can as quickly as they can, while still making sure they're asking the right kinds of questions that could help end up saving someone's life, and in the UK, in order to tackle the challenge, the National Health Service (NHS) recently announced they were rolling out a bot to help handle calls. Now though many more dispatchers could start getting additional support from another source, an Artificial Intelligence (AI) called Corti, an AI agent that dispatchers in Copenhagen first started trialling in 2016. Unlike other AI's what makes Corti unique is the fact that it can listen in on calls, understand words and sounds, and even recognise, just from verbal cues, the "sound" of heart attacks and other medical conditions, a technique that researchers in the USA have also been pursing with some success. Corti then prompts the emergency professional with the right questions to get a more accurate diagnosis.
Several studies being presented at the American College of Cardiology's 67th Annual Scientific Session demonstrate how the computer science technique known as machine learning can be used to accurately predict clinical outcomes in patients with known or potential heart problems. Collectively, the findings suggest that machine learning may usher in a new era in digital health care tools capable of enhancing health care delivery by aiding routine processes and helping physicians assess patients' risk.