infection


Can YOU tell which if these women are ill just by looking at them?

Daily Mail - Science & tech

It may be possible to spot if your relative, friend or colleague is ill just by looking at them, research suggests. Scientists injected volunteers with either E.coli or a placebo before asking others how sick they looked two hours later. The infected patients were judged to look'significantly worse', with people noticing their drooping eyelids and mouths. They also showed more negative facial expressions, which may be brought on by inflammation as the immune system fights off the infection. Researchers believe humans may have evolved the ability to pick up on subtle cues that suggest someone is contagious to avoid getting ill.


Viruses Have a Secret, Altruistic Social Life - Facts So Romantic

Nautilus

Reprinted with permission from Quanta Magazine's Abstractions blog. Social organisms come in all shapes and sizes, from the obviously gregarious ones like mammals and birds down to the more cryptic socializers like bacteria. Evolutionary biologists often puzzle over altruistic behaviors among them, because self-sacrificing individuals would at first seem to be at a severe disadvantage under natural selection. William D. Hamilton, one of the 20th century's most prominent evolutionary theorists, developed a mathematical theory to explain the evolution of altruism through kin selection--for instance, why most individual ants, bees and wasps forgo the ability to reproduce and instead pour all their efforts into raising their siblings. Bacteriologists developed game-theory models to explain why bacteria in groups produce metabolites for their neighbors, even though some cheaters take advantage of the situation.


I am not a robot. I'm a doctor and my patients need the real me.

USATODAY

The patient appeared to be dying. She had chronic lung disease, and she had been told she had little reserve left and had barely survived on home oxygen for the past few years. Each time she picked up a lung infection, the buzzards circled closer. Now she had tripped, fallen, broken a bone, had surgery, and her subsequent infection seemed to have pushed her past the point of no return. Still, I held off the palliative care/comfort care team for as long as I could, and she rallied.


Getting smart about artificial intelligence

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Genome sequencing currently produces a staggering 25 petabytes of digital information per year. A petabyte is 1015 bytes, or about 1,000 times the average storage on a personal computer. And there is no sign of a slowdown. The amount of DNA sequencing data produced around the world is doubling approximately every seven months. Computing power is also increasing, though more slowly.


Learning Graphs from Noisy Epidemic Cascades

arXiv.org Machine Learning

We consider the problem of learning the weighted edges of a graph by observing the noisy times of infection for multiple epidemic cascades on this graph. Past work has considered this problem when the cascade information, i.e., infection times, are known exactly. Though the noisy setting is well motivated by many epidemic processes (e.g., most human epidemics), to the best of our knowledge, very little is known about when it is solvable. Previous work on the no-noise setting critically uses the ordering information. If noise can reverse this -- a node's reported (noisy) infection time comes after the reported infection time of some node it infected -- then we are unable to see how previous results can be extended. We therefore tackle two versions of the noisy setting: the limited-noise setting, where we know noisy times of infections, and the extreme-noise setting, in which we only know whether or not a node was infected. We provide a polynomial time algorithm for recovering the structure of bidirectional trees in the extreme-noise setting, and show our algorithm matches lower bounds established in the no-noise setting, and hence is optimal. We extend our results for general degree-bounded graphs, where again we show that our (poly-time) algorithm can recover the structure of the graph with optimal sample complexity. We also provide the first efficient algorithm to learn the weights of the bidirectional tree in the limited-noise setting. Finally, we give a polynomial time algorithm for learning the weights of general bounded-degree graphs in the limited-noise setting. This algorithm extends to general graphs (at the price of exponential running time), proving the problem is solvable in the general case. All our algorithms work for any noise distribution, without any restriction on the variance.


The critical moment: can machine learning save lives in sepsis care? - Medical Technology Issue 12 February 2019

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If diagnosed early, the condition is easily treatable with antibiotics. However, sepsis is estimated to cause six million deaths a year around the world and claims a life every 3.5 seconds. The condition is so deadly because it is difficult to diagnose, as patients present the same symptoms that they would if they had the flu, gastroenteritis or a chest infection. Unfortunately, a misdiagnosis can often lead to death. For years, scientists have been working to create a device that would make it easier to diagnose sepsis, and now researchers from Massachusetts Institute of Technology (MIT) and Massachusetts General Hospital (MGH) have developed a predictive model that they think will help clinicians decide when to give potentially life-saving medication to sepsis patients.


How AI and Genomics Can Treat Epilepsy

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Epilepsy is among the most common neurological disorders that affects 65 million people of all ages globally. In the United States, 3.4 million Americans have epilepsy according to the CDC. Epilepsy can interfere with a person's ability to drive a car, play sports, swim, or exercise. It is a non-contagious brain disorder where recurrent, unprovoked seizures occur. Epilepsy may be caused by many factors, including traumatic brain injuries, stroke, loss of oxygen to the brain, brain tumor, parasitic brain infections (malaria, neurocysticercosis from tapeworms), viral infections (Zika, dengue, influenza), bacterial brain infections, neurological diseases, genetic predisposition, and other causes.


How AI and Genomics are used to treat Epilepsy

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Epilepsy is among the most common neurological disorders that affects 65 million people of all ages globally. In the United States, 3.4 million Americans have epilepsy according to the CDC. Epilepsy can interfere with a person's ability to drive a car, play sports, swim, or exercise. It is a non-contagious brain disorder where recurrent, unprovoked seizures occur. Epilepsy may be caused by many factors, including traumatic brain injuries, stroke, loss of oxygen to the brain, brain tumor, parasitic brain infections (malaria, neurocysticercosis from tapeworms), viral infections (Zika, dengue, influenza), bacterial brain infections, neurological diseases, genetic predisposition, and other causes.


Data61 using machine learning to track human infectious diseases in Australia

ZDNet

The Commonwealth Scientific and Industrial Research Organisation's (CSIRO) Data61 has developed a tool to track infectious diseases and how they specifically might spread to Australia, using Bayesian inference, a statistical machine learning method, for understanding the propensity of a region to spread disease to other regions. Using data from dengue virus outbreaks in Queensland as a case study, the tool identifies and tracks new cases of infection to their original source in Australia, and links how the disease has transferred between people. According to Data61 computer scientist Raja Jurdak, traditional methods of tracking infection routes often depend on time-consuming site investigations or interviews relating to travel routes of infected patients. Data61 has partnered with Queensland Health to obtain fully anonymised records of the reported dengue cases over a 15-year period. Jurdak told ZDNet these records serve as the ground-truth used to train the models.


IoT, patient engagement, RCM, genomics, deep learning among new tech at HIMSS19

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VivaLNK, a connected healthcare technology vendor, has introduced its Internet of Things-enabled medical wearable Sensor Platform, which comes with a range of sensors, edge computing technologies and an "Internet of Health Things" data cloud. This platform captures human vitals and biometrics, and delivers data from the patient to edge computing devices, as well as to the cloud, for application integration and analysis. Available through the VivaLNK Developer Program, the Sensor Platform enables IoT technology partners to capture streams of patient data such as heart and respiratory rates, temperature, ECG rhythms, activity and more. Partners such as Vitalic Medical, a digital health vendor specialising in the early detection of patient health deterioration and potential falls, is developing a bedside monitoring system using the platform. "Our growing aging patient population, rising complex health conditions and increasing staff workloads make it challenging for medical professionals to detect early signs of patient deterioration and prevent falls," Sue Dafnias, CEO of Vitalic Medical, said.