Health & Medicine


Local Doctor Completes 805th Robotic Surgery

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Dr. Stephen Szabo, an OB/GYN with Pinehurst Surgical Clinic (PSC), reached a milestone on Thursday, Sept. 19 with his 805th robotic surgery -- a hysterectomy with sacrocolpopexy and bladder suspension. Dr. Szabo first performed a robotically-assisted surgery in 2006 after coming to Pinehurst Surgical in 1998. He and Pinehurst Surgical Urologists Dr. Robert Chamberlain and Dr. Greg Griewe, along with Dr. Walter Fasolak, from FirstHealth's Southern Pines Women's Center, formed the core group of physicians who spearheaded the introduction of robotic surgery in Moore County. With 805 surgeries complete, Dr. Szabo is now in the company of an elite and distinguished group of surgeons practicing the art of robotic-assisted healthcare. The minimally invasive approach means that advanced gynecologic surgeries, which would have resulted in a three-to-five-day hospital stay, now only require a stay of three to five hours -- and carry a reduced risk of complications or infection.


Kansas City doctor uses 'vaping robot' in research

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Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. A Kansas City doctor is performing groundbreaking research on vaping, using a robot. Dr. Matthias Salathe spends a lot of time with e-cigarettes. "The notion was it's safe, and frankly we did not believe this," said Salathe.


Kansas City doctor uses 'vaping robot' in research

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Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. Dr. Matthias Salathe does the research in his lab at the University of Kansas Medical Center. A Kansas City doctor is performing groundbreaking research on vaping, using a robot. Dr. Matthias Salathe spends a lot of time with e-cigarettes. "The notion was it's safe, and frankly we did not believe this," said Salathe.


Doctor Bot: How artificial intelligence is already changing healthcare, and what's coming next

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Artificial intelligence is at the center of many emerging technologies today, and perhaps nowhere are the implications more meaningful than in healthcare. So where is AI making an impact in healthcare today? What will the future bring, and how should healthcare providers and technologists get ready? On the Season 4 premiere of GeekWire's Health Tech Podcast, we address all of those questions with three guests: Linda Hand, CEO of Cardinal Analytx Solutions, a venture-backed company that uses predictive technology to identify people at high risk of declining health, and match them with interventions; Colt Courtright, who leads Corporate Data & Analytics at Premera Blue Cross; and Dr. David Rhew, Microsoft's new chief medical officer and vice president of healthcare. This episode was recorded on location at the dotBlue conference in Seattle, hosted by the returning sponsor of the show, Premera Blue Cross.


ASTRO: AI predicts radiation side effects for cancer patients

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"Being able to identify which patients are at greatest risk would allow radiation oncologists to take steps to prevent or mitigate these possible side effects," Reddy added. "If the patient has an intermediate risk, and they might get through treatment without needing a feeding tube, we could take precautions such as setting them up with a nutritionist and providing them with nutritional supplements. If we know their risk for feeding tube placement is extremely high – a better than 50% chance they would need one – we could place it ahead of time so they wouldn't have to be admitted to the hospital after treatment. We'd know to keep a closer eye on that patient."



Φ Lab – Predictive Health Informatics at the University of Western Ontario

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Brent is a PhD Candidate in the Department of Computer Science and a member of the Φ Lab and Insight Lab. He was previously the instructor for MMASc 9251A: Professional Computing for Applied Scientists and presently the Teaching Assistant for Unstructured Data. As of March 1st 2019, Brent will also be a Mitacs Accelerate Intern. This work is with the Parkwood Institute and IBM with the target of improving mental health resources for Canadian Veterans. His research interests are two-fold.


Research on Artificial Intelligence and Primary Care: A Scoping Review

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Objective: The purpose of this study was to assess the nature and extent of the body of research on artificial intelligence (AI) and primary care. Methods: We performed a scoping review, searching 11 published and grey literature databases with subject headings and key words pertaining to the concepts of 1) AI and 2) primary care: MEDLINE, EMBASE, Cinahl, Cochrane Library, Web of Science, Scopus, IEEE Xplore, ACM Digital Library, MathSciNet, AAAI, arXiv. Screening included title and abstract and then full text stages. Final inclusion criteria: 1) research study of any design, 2) developed or used AI, 3) used primary care data and/or study conducted in a primary care setting and/or explicit mention of study applicability to primary care; exclusion criteria: 1) narrative, editorial, or textbook chapter, 2) not applicable to primary care population or settings, 3) full text inaccessible in the English Language. We extracted and summarized seven key characteristics of included studies: overall study purpose(s), author appointments, primary care functions, author intended target end user(s), target health condition(s), location of data source(s) (if any), subfield(s) of AI.


Predicting the risk of emergency admission with machine learning: Development and validation using linked electronic health records

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We used longitudinal data from linked electronic health records of 4.6 million patients aged 18–100 years from 389 practices across England between 1985 to 2015. The population was divided into a derivation cohort (80%, 3.75 million patients from 300 general practices) and a validation cohort (20%, 0.88 million patients from 89 general practices) from geographically distinct regions with different risk levels. We first replicated a previously reported Cox proportional hazards (CPH) model for prediction of the risk of the first emergency admission up to 24 months after baseline. This reference model was then compared with 2 machine learning models, random forest (RF) and gradient boosting classifier (GBC). The initial set of predictors for all models included 43 variables, including patient demographics, lifestyle factors, laboratory tests, currently prescribed medications, selected morbidities, and previous emergency admissions.


Mayo Clinic taps Google Cloud as strategic partner to accelerate innovation in AI, analytics and digital tools

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Mayo Clinic has entered into a 10-year strategic partnership with Google to use the tech giant's cloud platform to accelerate innovation through digital technologies. Terms of the deal were not disclosed. The Rochester, Minn.-based hospital said it selected Google Cloud to be the cornerstone of its "digital transformation." As part of the collaboration, Mayo Clinic will store patient data in the cloud and use advanced cloud computing, data analytics, machine learning, and artificial intelligence to advance the diagnosis and treatment of disease, hospital executives said in a press release. "Data-driven medical innovation is growing exponentially, and our partnership with Google will help us lead the digital transformation in health care," Gianrico Farrugia, M.D., president and CEO of Mayo Clinic, said in a statement.