Talk by Suggestic’s CEO Victor Chapela on the Hyper Wellbeing 2016 conference. Augmenting Human Decision Making – Machine Intelligence Could Help Eradicate Most Chronic Diseases
These superheroes are as real as they get. They are, in fact, scientists, doctors and clinicians continuously tinkering in tech labs across Singapore. Meet the individuals facing off against some of the world’s most pressing health issues — from cancer to heart disease and diabetic retinopathy, as they harness the incredible power of Artificial Intelligence. The Hidden Layer: Healthcare Trailblazers ============== #CNAInsider #TheHiddenLayerHealthcareTrailblazersCNA #HealthTech #ArtificialIntelligence #Documentary #Singapore #Cancer #HeartDisease #Diabetics #AI SUBSCRIBE to CNA INSIDER for more informative content: https://cna.asia/insideryoutubesub Follow CNA INSIDER on: Instagram: https://www.instagram.com/cnainsider/ Facebook: https://www.facebook.com/cnainsider/ Website: https://cna.asia/cnainsider
Millions of patients live in rural or developing areas where disease treatment isn’t the problem: it’s overcoming the 1:1,000 doctor to patient ratio and screening for preventable diseases. High school student Kavya Kopparapu shares the future of artificial intelligence in this field– as a substitute for doctors in these areas to deliver much-needed medical diagnoses.  Kavya Kopparapu is the Founder and CEO of GirlsComputingLeague and current junior at Thomas Jefferson High School for Science and Technology. She is dedicated to sharing her passion for computer science with others, especially young girls, as the field has given her a world of opportunity, and has been recognized by organizations such as the White House and the National Center for Women in Information Technology (NCWIT). Her journey with computer science began in elementary school, when she was introduced to the Scratch programming language and developed robots using the Mindstorms programming language. Her interests were further strengthened when she took AP Computer Science in freshman year, followed by classes like Artificial Intelligence and Computer Vision. This talk was given at a TEDx event using the TED conference format but independently organized by a local community. Learn more at https://www.ted.com/tedx
If you are interested to hear more about the advancements of Artificial Intelligence in Bioscience, join Special Interest Group on Artificial Intelligence in Biomedicine http://benevolent.ai/blog/benevolentai/join-the-special-interest-group-on-artificial-intelligence-in-biomedicine/ Suchi Saria, Assistant Professor of Computer Science at Johns Hopkins University gave a talk at the Artificial Intelligence in Bioscience Symposium 2017 on how machine can spot diseases faster than human: We’re collecting measurements related to our health and well-being at an unprecedented pace. The talk is focusing on the use of data from wearables and electronic health records for developing tools that enhance care delivery and also highlights success stories and describes open challenges where new innovations are needed.
Some chronic conditions, such as the autoimmune disease scleroderma, are especially difficult to treat because patients exhibit highly variable symptoms, complications and treatment responses. The process of finding an effective treatment for an individual can be frustrating for doctors, and painful and expensive for patients. With support from the National Science Foundation (NSF), computer scientist and professor Suchi Saria, with Dr. Fredrick Wigley and an interdisciplinary team of experts at Johns Hopkins University, is leading a groundbreaking effort using Big Data to ease some of that pain for scleroderma patients. The team’s research is in machine learning, a subfield of computer science and statistics that allows machines to learn from data. The team designs statistical algorithms that enable computers to analyze large volumes of medical records and identify subgroups of patients with similar patterns of disease progression. In addition, the system learns the symptoms and treatments that are predictive of specific patterns of improvement or deterioration to help doctors pick the right set of treatments for an individual patient. Doctors can then map the course of treatment for new patients, based in part on what the computers reveal about what happened to other patients with similar symptoms. Saria foresees data analysis similar to this helping clinicians treat other chronic diseases, such as lupus and rheumatoid arthritis. Research in this episode was supported by NSF award #1418590, Smart and Connected Health (SCH)/Integrative Projects (INT): Collaborative Research: Modeling Disease Trajectories in Patients with Complex, Multiphenotypic Conditions. NSF Grant URL: http://www.nsf.gov/awardsearch/showAward?AWD_ID=1418590&HistoricalAwards=false Miles O’Brien, [More]
Keynote by Suchi Saria Subscribe to O’Reilly on YouTube: http://goo.gl/n3QSYi Follow O’Reilly on Twitter: http://twitter.com/oreillymedia Facebook: http://facebook.com/OReilly Google: http://plus.google.com/+oreillymedia