- IBM announced IBM Machine Learning, a cognitive platform using large data repositories to create and deploy high volume analytic models in the healthcare private cloud.
The new release aims to assist organizations in developing and testing analytic models more quickly with less IT staff.
IBM built its new private cloud machine learning platform by extracting the core machine learning technology from IBM Watson.
The new artificial intelligence (AI) solution allows data scientists to automate the creation training and development of operational analytic models that support any programming language, popular machine learning frameworks (e.g., Apache, SparkML, TensorFlow, H2O), and any transactional data type. It will also allow data scientists to automate models that have a lower security risk by keeping the data on-premise.
IBM Machine Learning deploys Cognitive Automation for Data Scientists from IMB Research to assist data scientists in choosing the correct algorithm to suit an organization’s unique needs. Organizations are matched with algorithms based on what is needed and how fast the algorithm needs to produce results.
The solution uses the IBM z Systems mainframe, which can process up to 2.5 billion transactions in a day. With z Systems, IBM Machine Learning can more effectively extract data without moving the data off-premise and away from the private cloud. This lowers the risk of a security breach in transit and allows the solution to constantly analyze the data.
IBM Machine Learning has already had a successful deployment in a healthcare environment. Argus Health is using the solution to help payers and providers manage the increased complexity of data.
Currently, Argus Health is testing IBM Machine Learning and is assessing the training and deployment applications to better manage costs. The healthcare organization aims to leverage data using the machine learning capabilities of IBM to create applications and solutions that can improve patient care.
"Helping our health plan clients achieve the best clinical and financial outcomes by getting the best care delivered in the most appropriate place is the mission of Argus while focused on the vision of becoming preeminent in providing pharmacy and healthcare solutions," Argus Health President Marc Palmer said in a statement. "We are excited about the possibilities and the potential we have seen from IBM Machine Learning working in concert with our RxNova claims processing platform, clinical solutions, and applied analytics in creating models that are constantly improving by using new data and enabling real-time results to the benefit of members, their caregivers and physicians."
IBM Analytics General Manager Rob Thomas said that machine learning gives organizations the opportunity to explore massive amounts to stored data and is considered the last frontier in analytics. He believes that these technologies will be critical in automating insight into critical systems and cloud services.
IBM’s goal with the continued growth and sophistication of its AI solutions is to grow machine learning quickly in the data center so organizations can make us of it.
In the long term, machine learning will help healthcare organizations tailor care by having the AI analytics solution provide unique results to each patient.
IBM Watson has been making significant progress in healthcare over the past year through partnerships with various healthcare organizations.
Back in October 2016, IBM Watson partnered with Teva Pharmaceuticals to discover new treatment options and improve chronic disease management using Watson in the cloud. The collaboration combined Teva’s therapeutic technologies with IBM Watson’s cognitive computing to clinicians and patients to better understand and control chronic conditions.
Last month, IBM Watson Health announced a new partnership with the FDA to use AI to discover how to apply blockchain technology to address current challenges in securely exchanging healthcare big data. The two-year research initiative will explore blockchain’s potential for sharing owner-mediated data sources, including information from electronic health records, Internet of Things (IoT) devices, and precision medicine data sources.