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Are you a Technology Investor? Wanting to invest in a cutting edge Artificial Intelligence Cancer Research opportunity?

Posted by tony

March 7, 2018

Are you a Technology Investor? Wanting to invest in a cutting-edge Artificial Intelligence Cancer Research opportunity? Contact us!

Sherlock Big Data Knowledge Discovery and Analysis (Big Data-KDA) tool will provide clinicians and researchers with the ability to perform Big Data Analytics on Non-Small Cell Lung Cancer (NSCLC) data.

The Sherlock NSCLC API is designed to harness NSCLC Big Data to enable researchers to better target NSCLC treatments and to enhance the decision-making process to improve patient care.

According to our research, there are approximately 22,000 new cases of lung cancer each year. About 85-90% of lung cancers are NSCLC with an overall 5-year survival rate of only ~18 percent (American Cancer Society). The economic burden of lung cancer just based on per patient cost is estimated $46,000 per patient (lung cancer journal). While treatment efforts using drugs and chemotherapy are effective for some, more effective treatment has been hampered by the inability of clinicians to better target treatments to patients. However, our product Sherlock Big Data KDA will improve treatments by exploring the wealth of cancer-related knowledge that has been accumulated in many forms and sources.

Our research has also determined that big-data analysis will help to identify which cancer patients are most likely to respond to specific therapeutic approaches. Analysis of such data will also improve drug development by allowing researchers to better target novel treatments to patient populations. The American Cancer Society indicates that filtering countless health websites for relevant, accurate, and trustworthy information is daunting, and it is even more difficult to draw insights from multiple sources (American Cancer Society, 2016). In addition to the technical challenges, HIPAA makes it difficult for researchers to tap into large caches of clinical and genomic data shared across multiple institutions or firms. Currently, leading applications lack the necessary capabilities of Deep-learning, Self-Learning, and Ontology modification, necessary to properly analyze cancer Big Data repositories. Our product will deliver on these aspects and more when it comes to cancer research.

Contact our office to learn more and to join us in our fight against cancer!

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