Researchers have studied the medical histories of the entire population of Denmark to chart how medical conditions are linked and forecast disease before it begins.
In a major advance for the field of biomedical Big Data analytics, scientists followed the medical history of some 6.2 million Danes over the course of almost 15 years. Since the dataset includes those who died in those years, that’s a sample size 600,000 people larger than the current living population of the small Scandinavian country. Using the Danish National Patient Registry, which healthcare providers are required to report to, the data scientists were given access to 65 million inpatient, outpatient and emergency room events from 1996 to 2010.
Over that long study period and with so many data points that included every demographic in the country, they were able to start seeing hidden patterns in how disease progresses from its earliest stages. They found more than 1,100 “sequential diagnostic correlations” that occurred the most frequently in the Danish population, from an early seemingly unrelated medical issue through later diagnosis of maladies like diabetes, chronic obstructive pulmonary disease, cancer, arthritis and cardiovascular disease.
See below for an example of a disease network.
Wikipedia isn’t just a website that helps students with their homework and settles debates between friends. It can also help researchers track influenza in real time.
A new study released in April in the journal PLOS Computational Biology showcased an algorithm that uses the number of page views of select Wikipedia articles to predict the real-time rates of influenza-like illness in the American population.
Influenza-like illness is an umbrella term used for illnesses that present with symptoms like those of influenza, such as a fever. These illnesses may be caused by the influenza virus, but they can have other causes as well. The Centers for Disease Control and Prevention publish data on the prevalence of influenza-like illness based off a number of factors like hospital visits, but the data takes two weeks to come out, so it’s of little use to governments and hospitals that want to prepare for influenza outbreaks.
Troops going out for their morning patrol strap on combat helmets, armored tactical vests and other protective devices to counter the ever-present possibility of injury in hostile territory. Many mount shielded vehicles designed to thwart the devastation wrought by rocket-propelled grenades, mines and the like.
Increasingly, medical and equipment researchers are adding another layer that will help protect future combatants—Big Data. At least two separate projects are collecting reams of information on trauma sustained by improvised explosive devices and other battlefield injuries. The object: to suss out equipment improvements, more effective treatments and better patient outcomes.
Trees don’t make work easy for the scientists who want to study them and the landscapes they create, since the leaves, fruits and flowers of a forest’s canopy generally stretch far above human reach. So, a team of researchers is turning to drones, programming these pilot-less flying machines to buzz over forests while snapping pictures.
“I flip the switch on the controller and away it goes on its own,” says Jonathan Dandois, a doctoral candidate at the University of Maryland, Baltimore County, as he launched a drone over a patch of forest on campus on an overcast October day. “It will climb up to about 100 meters above and will continue on its route.”