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Identifying Health Risks Using Pattern Recognition and AI Posted on Sep 12 - 2017

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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.

Enhancing Patient Care with Predictive Analytics

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. View More
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