As machine learning technology continues to advance at a rapid pace, providers are excited by the potential of this type of artificial intelligence to predict which patients are most at risk for clinical events that require early intervention. However, these medical breakthroughs are being hampered by the lack of health data necessary to learn the complex patterns required to positively affect patient care. All Health Data Management content is archived after seven days.

As machine learning technology continues to advance at a rapid pace, providers are excited by the potential of this type of artificial intelligence to predict which patients are most at risk for clinical events that require early intervention. However, these medical breakthroughs are being hampered by the lack of health data necessary to learn the complex patterns required to positively affect patient care. That's the consensus of healthcare stakeholders who gathered at Wednesday's Machine Learning in Healthcare: Industry Applications conference in Boston to discuss the technology's promise and challenges. Research published earlier this month by MarketsandMarkets projected that the healthcare artificial intelligence market is expected to grow from $667.1 million in 2016 to more than $7.9 billion by 2022, a compound annual growth rate of 53 percent over the forecast period. Machine learning technology is accelerating at a rate beyond Moore's Law, with algorithms and models doubling in capability every six months. In fact, a study presented this week at the American Thoracic Society International Conference in Washington showed that a machine-learning algorithm has the capability to identify hospitalized patients at risk for severe sepsis and septic shock using data from electronic health records.