Maternal-fetal medicine specialists like us are tasked with caring for women with "high-risk" pregnancies, usually defined as pregnancies complicated by chronic or acute maternal illness, fetal concerns, or problems related to pregnancy itself (e.g. Our nuclear event--one of the worst things that can happen when we practice--is a mother dying. Maternal mortality in the United States is rare but, sadly, nowhere near rare enough: Data collected from 1990–2015 show that the number of maternal deaths per 100,000 births has increased from 16.9 1990 to 26.4 in 2015. Not only are more American mothers dying than in our peer countries, but we're one of the only developed countries where the death rate is increasing, not decreasing.
Using machine learning, the researchers trained an AI using over eight years of patient health data. The researchers said that their experiment demonstrates that an AI can assist doctors in making an accurate, semi-automated analysis of medical scans, allowing patients to receive better treatment. "The AI really allows you to tailor the individual treatment," researcher Declan O'Regan told BBC News. In November, for example, Alphabet Inc. announced that its DeepMind AI team had teamed with the U.K.'s National Health Service to improve patient care using AI.
IBM provided an Analytics Assessment & Insights Impact Grant to the Shenzhen CDC to help further their mission of infectious disease prevention and control, shouldering the monitoring, alarming and treatment of emergency public health events in Shenzhen for the 12 million citizenry by building a self adaptive online machine learning module that provides cognitive-based modeling for epidemic disease prediction and analysis on case number and trend. The output of this work has allowed the organization to develop prediction models to help forecast seasonal flu outbreaks and provide information to citizens on affected areas. CDC organizations across China are now considering the implementation of this solution to help track flu and other infectious diseases.
"We are just starting to discover the countless ways we can apply cognitive computing to healthcare," said Ryan Pellet, senior vice president of consulting and services for Welltok. "We are excited to have addressed a costly and cumbersome issue with our proprietary technology and IBM Watson, and will continue to explore opportunities to simplify the consumer's experience and drive new, more effective ways to engage with and satisfy them."
Laura received her PhD in Biostatistics from the University of Minnesota and Sherri completed her PhD in Biostatistics at UC Berkeley. SimplyStats: What is the Health Policy Data Science Lab? Are they mostly focused on health policy?_** Rose: One of the fun things about working in health policy is that it is quite expansive. In a recent project, I've been studying how giving people a tool to look up prices for medical services changes their health care spending.
Researchers at Regenstrief Institute and Indiana University School of Informatics and Computing say they now can detect cancer cases using data from free-text pathology reports at least as well--and faster--than clinicians reviewing reports manually. The researchers used existing data algorithms and open source machine learning tools to create a breakthrough electronic approach that could significantly speed patient diagnoses and public health reporting. At Regenstrief/IU, machine learning identified patterns of language in pathology reports, enabling algorithms to create a rule that if certain factors or findings are found in the automated pathology review, then a patient is likely to have cancer. But Indiana could be a good test bed for the technology, as the state as had automated public health surveillance reporting--currently, 40 notifiable diseases to report to public health agencies, since 2000, Grannis contends.
Two announcements yesterday (April 21) suggest that deep learning algorithms rival human skills in detecting cancer from ultrasound images and in identifying cancer in pathology reports. Samsung Medison, a global medical equipment company and an affiliate of Samsung Electronics, has just updated its RS80A ultrasound imaging system with a deep learning algorithm for breast-lesion analysis. Meanwhile, researchers from the Regenstrief Institute and Indiana University School of Informatics and Computing at Indiana University-Purdue University Indianapolis say they've found that open-source machine learning tools are as good as -- or better than -- humans in extracting crucial meaning from free-text (unstructured) pathology reports and detecting cancer cases. Everything -- physician practices, health care systems, health information exchanges, insurers, as well as public health departments -- are awash in oceans of data.
To support public health reporting, the use of computers and machine learning can better help with access to unstructured clinical data--including in cancer case detection, according to a recent study. Often, the unstructured free text data made available by electronic health records is obtained by means that are "resource intensive, inherently complex and rely on structured clinical data and dictionary-based approaches," according to the authors of the study, published in the Journal of Biomedical Informatics. The researchers, from the Regenstrief Institute and Indiana University-Purdue University in Indianapolis, used about 7,000 pathology reports from the Indiana health information exchange to attempt to detect cancer cases using already available algorithms and open source machine learning tools. Stanford University researchers also found success in using analysis of free-text notes in electronic health records for surveillance of drug interactions in near real time, adding that the evolution of better tools in natural language processing will help speed up the process.
Albrecht, Stefano V. (University of Edinburgh) | Beck, J. Christopher (University of Toronto) | Buckeridge, David L. (McGill University) | Botea, Adi (IBM Research, Dublin) | Caragea, Cornelia (University of North Texas) | Chi, Chi-hung (Commonwealth Scientific and Industrial Research Organisation) | Damoulas, Theodoros (New York University) | Dilkina, Bistra (Georgia Institute of Technology) | Eaton, Eric (University of Pennsylvania) | Fazli, Pooyan (Carnegie Mellon University) | Ganzfried, Sam (Carnegie Mellon University) | Giles, C. Lee (Pennsylvania State University) | Guillet, Sébastian (Université du Québec) | Holte, Robert (University of Alberta) | Hutter, Frank (University of Freiburg) | Koch, Thorsten (TU Berlin) | Leonetti, Matteo (University of Texas at Austin) | Lindauer, Marius (University of Freiburg) | Machado, Marlos C. (University of Alberta) | Malitsky, Yui (IBM Research) | Marcus, Gary (New York University) | Meijer, Sebastiaan (KTH Royal Institute of Technology) | Rossi, Francesca (University of Padova, Italy) | Shaban-Nejad, Arash (University of California, Berkeley) | Thiebaux, Sylvie (Australian National University) | Veloso, Manuela (Carnegie Mellon University) | Walsh, Toby (NICTA) | Wang, Can (Commonwealth Scientific and Industrial Research Organisation) | Zhang, Jie (Nanyang Technological University) | Zheng, Yu (Microsoft Research)
AAAI's 2015 Workshop Program was held Sunday and Monday, January 25–26, 2015 at the Hyatt Regency Austin Hotel in Austion, Texas, USA. The AAAI-15 workshop program included 15 workshops covering a wide range of topics in artificial intelligence. Most workshops were held on a single day. The titles of the workshops included AI and Ethics, AI for Cities, AI for Transportation: Advice, Interactivity and Actor Modeling, Algorithm Configuration, Artificial Intelligence Applied to Assistive Technologies and Smart Environments, Beyond the Turing Test, Computational Sustainability, Computer Poker and Imperfect Information, Incentive and Trust in E-Communities, Multiagent Interaction without Prior Coordination, Planning, Search, and Optimization, Scholarly Big Data: AI Perspectives, Challenges, and Ideas, Trajectory-Based Behaviour Analytics, World Wide Web and Public Health Intelligence, Knowledge, Skill, and Behavior Transfer in Autonomous Robots, and Learning for General Competency in Video Games.
Albrecht, Stefano V. (University of Edinburgh) | Barreto, André M. S. (Brazilian National Laboratory for Scientific Computing) | Braziunas, Darius (Kobo Inc.) | Buckeridge, David L. (McGill University) | Cuayáhuitl, Heriberto (Heriot-Watt University) | Dethlefs, Nina (Heriot-Watt University) | Endres, Markus (University of Augsburg) | Farahmand, Amir-massoud (Carnegie Mellon University) | Fox, Mark (University of Toronto) | Frommberger, Lutz (University of Bremen) | Ganzfried, Sam (Carnegie Mellon University) | Gil, Yolanda (University of Southern California) | Guillet, Sébastien (Université du Québec à Chicoutimi) | Hunter, Lawrence E. (University of Colorado School of Medicine) | Jhala, Arnav (University of California Santa Cruz) | Kersting, Kristian (Technical University of Dortmund) | Konidaris, George (Massachusetts Institute of Technology) | Lecue, Freddy (IBM Research) | McIlraith, Sheila (University of Toronto) | Natarajan, Sriraam (Indiana University) | Noorian, Zeinab (University of Saskatchewan) | Poole, David (University of British Columbia) | Ronfard, Rémi (University of Grenoble) | Saffiotti, Alessandro (Orebro University) | Shaban-Nejad, Arash (McGill University) | Srivastava, Biplav (IBM Research) | Tesauro, Gerald (IBM Research) | Uceda-Sosa, Rosario (IBM Research) | Broeck, Guy Van den (Katholieke Universiteit Leuven) | Otterlo, Martijn van (Radboud University Nijmegen) | Wallace, Byron C. (University of Texas) | Weng, Paul (Pierre and Marie Curie University) | Wiens, Jenna (University of Michigan) | Zhang, Jie (Nanyang Technological University)
The AAAI-14 Workshop program was held Sunday and Monday, July 27–28, 2012, at the Québec City Convention Centre in Québec, Canada. The AAAI-14 workshop program included fifteen workshops covering a wide range of topics in artificial intelligence. The titles of the workshops were AI and Robotics; Artificial Intelligence Applied to Assistive Technologies and Smart Environments; Cognitive Computing for Augmented Human Intelligence; Computer Poker and Imperfect Information; Discovery Informatics; Incentives and Trust in Electronic Communities; Intelligent Cinematography and Editing; Machine Learning for Interactive Systems: Bridging the Gap between Perception, Action and Communication; Modern Artificial Intelligence for Health Analytics; Multiagent Interaction without Prior Coordination; Multidisciplinary Workshop on Advances in Preference Handling; Semantic Cities -- Beyond Open Data to Models, Standards and Reasoning; Sequential Decision Making with Big Data; Statistical Relational AI; and The World Wide Web and Public Health Intelligence. This article presents short summaries of those events.