Diagnostics

Machine learning can tease out which location-specific—or “area-level”—social determinants of health warrant close monitoring in people who have diabetes and are at risk of losing control over it.  

It’s not unusual for hospitalized patients to take a sudden turn for the worse. A continuous inspection of electronic medical records by machine-learning algorithms can warn of impending trouble in real time, giving physicians a chance to proactively intervene.

Using a dataset of records from nearly 3 million pediatric patients, South Korean researchers have developed and validated a deep-learning algorithm that can tell emergency doctors which children will need to be admitted to critical-care units.

Hospital inpatients who are likely to turn violent can be identified by algorithmic analysis of routine clinical notes stored in electronic health records, according to a study published in JAMA Network Open July 3.

Healthcare AI isn’t yet good enough to reliably deliver on its promises where it stands to make the biggest difference—and it doesn’t have enough high-quality data to get there anytime soon.

AI is enabling healthcare workers to understand people’s moods with a level of accuracy that may be as unnerving as it is exciting.

Researchers have demonstrated two machine learning techniques that, when combined to analyze social-media posts, can boost early detection of clinical depression by 10% over the current state of the art.

AI is aiding in the diagnosis of several diseases and health risks, and researchers have recently found that machine learning can help predict the onset of psychosis, a debilitating mental disorder that can compromise an individual’s psychology and their ability to think and feel.

Combining AI with advances in acoustic engineering, Australian researchers have developed an algorithm that can help diagnose common pediatric respiratory conditions such as asthma, croup and pneumonia.

Stanford researchers have developed an AI tool that can help diagnose damaging brain aneurysms that can have potentially fatal effects.

Using a deep neural network equipped with all patient information relevant to diagnosing adult asthma, researchers in Japan have achieved 98% diagnostic accuracy.

Researchers have developed a deep-learning framework that can show how mutations in “noncoding DNA”—meaning parts of the strand that contain no genes—contribute to autism. And they believe their algorithm is generalizable for clinical researchers studying the role of noncoding mutations in just about any disease.