Identifying Health Risks Using Pattern Recognition and AI

Data is the most valuable currency in every industry. It is the foundation for IT innovation, business growth, and for the life sciences sector, saving lives.

The world of Big Data is expanding rapidly, and organizations will need advanced IT solutions to keep up. Many life sciences organizations are turning to NVIDIA GPU-accelerated high performance computing (HPC) to help them manage their most demanding applications and workloads. With proliferating volumes of patient, business, and historical data—in the form of patient histories, electronic health records, clinical trial results, IoT insights, and more—IT departments are seeking next-generation technologies that can help them treat the patients of today and combat the health challenges of tomorrow.


Artificial intelligence (AI), an approach in which machines employ data analytics to recognize patterns and make decisions, is driving a major paradigm shift in life sciences disciplines. Much like the neural pathways of the human brain, cognitive computing enables computers to synthesize patient information quickly and accurately in which to identify and even predict health risks. Gaining real-time insights into a patient’s health can help prevent hospitalizations, assess the risk of disease, determine the best course of treatment, and streamline care delivery.

According to a study by the Association of American Medical Colleges, there will be a shortfall of 14,900–35,600 primary care physicians by 2025 to treat a growing elderly population. AI applications, including deep learning and predictive analytics, are increasingly being applied to life science operations to harness the full power of their medical data, recognize existing or potential health risks, and respond to critical insights in real-time. In fact, physicians can improve the accuracy of their medical outcomes by 50–70 percent, and at 50,000X faster speed with AI technologies. These technological advancements are empowering physicians with superhuman intelligence to deliver more effective, proactive, and quality care.

Predictive analytics can benefit life sciences organizations in a number of ways:

·      Increase the accuracy of diagnoses

·      Improve preventive medicine and public health

·      Enhance personalized care

·      Accurately predict insurance costs

·      Streamline research and development with prediction models

·      Guide drug development to deliver medications that meet public need

·      Better patient outcomes


To make the most of their data, life sciences organizations must invest in HPC solutions that are designed to accelerate complex analytics workloads. Adopting a powerful GPU-accelerated server platform is the first step to effectively manage AI applications, and with a scalable and flexible infrastructure, organizations can achieve density-optimized IT performance, greater power efficiency, lower total cost of ownership, and superhuman cognitive capabilities.

Savvy healthcare and Pharma companies are turning to NVIDIA GPU-accelerated computing to revolutionize analytics to solve great challenges. Reducing time to insight not only enables physicians to diagnose and treat patients more efficiently, but it also improves patient outcomes and the overall quality of care. Computing with GPUs works to streamline healthcare operations and drive more informed, patient-centric decisions.

Identifying health risks is vital to improving public health and caring for an aging population. Pattern recognition and AI are empowering life sciences organizations to operate more efficiently and effectively than ever. To learn more about the benefits of predictive analytics, visit me on Twitter at @wheatHPC. I also invite you to follow @HPE_HPC@NVIDIA, and @NvidiaAI for the latest news on computing in healthcare.

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