The researcher is expected to have (i) a strong background in machine learning, and (ii) motivation to pursue multidisciplinary research at the …

Decreasing costs and rise in number of services in this segment are expected to drive segment with lucrative avenues.

For more than ten years, Achieve, an Acquia partner, has been bringing innovative portal solutions to healthcare providers with a user-centered focus. They make the most complex web development projects possible for companies like Children's Hospital Los Angeles, Universal Music Group, Dexcom, The Recording Academy, and Scripps Translational Science Institute. Achieve sought out Acquia to participate in their latest Digital Health Innovations (DHI) event because of Acquia's involvement with technical trends like machine learning that are currently impacting the healthcare industry. Katherine Bailey, Principal Data Scientist at Acquia, was the featured speaker at the event. Like past DHI events, this one continued Achieve's aim of bringing the San Diego tech, healthcare, and life science communities together through thought leadership.

There are two fields with seemingly endless career opportunities: healthcare and computer science. And when medical care intersects with technology, the possibilities are life changing. Machine learning introduces new insights to healthcare professionals through compiling big data to improve the way doctors and clinicians can diagnose, treat and even predict outcomes for their patients, according to Finale Doshi-Velez (pictured), assistant professor of Computer Science at Harvard's John A. Paulson School of Engineering and Applied Sciences. Doshi-Velez is on the front lines of educating and researching tangible ways to improve mental health through machine learning. She is working with students in several areas, but her focus is on machine learning for healthcare applications focused on dissecting the autism spectrum and helping to alleviate depression. Doshi-Velez spoke with Lisa Martin (@Luccazara), co-host of theCUBE, SiliconANGLE Media's mobile live streaming studio, at the Stanford Global Women in Data Science (WiDS) Conference in Stanford, CA, about her work in these developing fields and how she is preparing the next generation by teaching students the emerging skills they will need in a new workplace.

The last few years have seen a number of fascinating case studies that have used machine learning to produce medical diagnoses. There are a couple of new examples from both a corporate giant and a nimbler startup. On the giant side of the fence is a traditional player in this space. Researchers at Google have developed an algorithm for scanning our eyes in order to better spot a particularly common form of blindness. The project follows a familiar path, feeding the algorithm a bunch of medical images of the retina to train it to look for diabetic retinopathy, which is a condition believed to affect around 1/3 of diabetes patients.