Скачать или смотреть Using AI and Predictive Algorithms to Help Advanced Cancer Patients Identify the Best Treatment Opti

Presented By: Kenny Wong


Speaker Biography: Kenny is currently the Chief Product Officer at xCures, a company that is focusing on supporting advanced cancer patients and their doctors with evidence-based treatment options while capturing outcomes and learning in real-time to accelerate the future of oncology. He has over a decade of product leadership/management experience in growing innovative healthcare products. Most recently, he was the Vice President of Products at GenomeSmart which was acquired by Ambry Genetics in 2020, a subsidiary of Konica Minolta. Prior to that, he was the Senior Product Director for Counsyl, which was acquired by Myriad Genetics in 2018. Kenny developed his passion for helping patients early in his career as a board-certified genetic counselor at UCSF Medical Center. He holds a B.Sc from University of British Columbia in Cell Biology and Genetics and a M.S. in Human Genetics from Sarah Lawrence College.


Webinar: Using AI and Predictive Algorithms to Help Advanced Cancer Patients Identify the Best Treatment Options


Webinar Abstract: Advanced cancer patients, individuals that have a less than 50% chance of 5 year-survival and have exhausted standard of care options, are often seeking for innovative therapies. These drugs may be investigational and only available via clinical trials. Unfortunately, over 95% of cancer patients are excluded owing to exclusion criteria, geographic limitations, timing issues, or simply being at the wrong institution at the wrong time. Alternatively, they might be trying off-label treatments or novel combination therapies via a wide variety of means (right-to-try, expanded access, novel combination therapy, novel indications, etc..). Identifying the right cancer treatment is difficult. Cancer is highly personal, and what works for one person might not work for someone else. Unfortunately, unless a paper is published or a case series documented, most of the learning from these n-of-1 experiments is lost. Finally, oncologists often don't have enough time to help patients find the best treatment options due to the increasing demand of oncology services from factors including an aging and growing population. In fact, ASCO estimates that there will be a shortage of over 2200 oncologists by 2025.

We will discuss how artificial intelligence (AI) and natural language processing (NLP) tools can be utilized to analyze Real World Data (RWD) and other data sources to empower patients and their oncologists to make more informed and effective treatment decisions. Furthermore, collecting the safety and efficacy information of these therapies can also be beneficial for researchers of investigational drugs, new therapeutic combinations, or novel indications. This RWD can increase the ability to identify causal inferences between treatments and outcomes.


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