From a Health IT Analytics online release:
The prototype revealed that using artificial intelligence and machine learning to examine certain combinations of vital signs and other biomarkers could strongly predict the likelihood of infection up to 48 hours in advance of clinical suspicion, including observable symptoms.
Royal Philips, in collaboration with the Defense Threat Reduction Agency (DTRA) and Defense Innovation Unit (DIU) of the US Department of Defense (DoD), are building a machine learning algorithm that will be able to detect an infection before a patient shows signs or symptoms.
The partnering organizations recently announced results from an 18-month project, called Rapid Analysis of Threat Exposure (RATE), the first large-scale exploration of pre-symptomatic infection in humans. The project aims to develop an early warning system that accelerates diagnosis and treatment of infection, containing the spread of communicable disease.
To read more: https://healthitanalytics.com/news/philips-dod-build-machine-learning-system-to-detect-infection
The top 10 most commonly administered antibiotics in the ER for nonadmitted patients were:
“The risk for dementia is elevated about twofold in people who have diabetes or 
PayScale reports real-time salary data from over 54 million reports from job seekers, fact checking the data against private and public compensation data.
At Dr. Soong’s hospital, withholding the results of urine cultures, unless doctors actually called the microbiology lab to request them, 
Most Americans probably aren’t aware of the decline in the number of individuals training to become transplant physicians and how it will affect the future of medicine. Neither are the 2020 presidential hopefuls, all of whom have policies they believe best provide health care coverage for Americans without acknowledging or calling attention to the fact that soon there may not be enough doctors to do the work once more people are insured. We need a plan for that.
Our 3D deep-learning system performed well in both primary and external validations, suggesting that it could potentially be used for automated detection of glaucomatous optic neuropathy based on SDOCT volumes. Screening with the deep-learning system is much faster than conventional glaucoma screening methods (ie, by experienced specialists), can be done automatically, and does not require a large number of trained personnel on site. Further prospective studies are warranted to estimate the incremental cost-effectiveness of incorporating this artificial intelligence-based model for screening for glaucoma, both in the general population and among at-risk people.
But greater use of biosimilars could create significantly more savings. If biosimilars obtained a 75 percent market share, less than the share of these medicines in many European Union nations, the resulting annual savings for the U.S. healthcare system could be nearly $7 billion, based on Winegarden’s analysis.
The irony is 
Intellicurious 