In this episode, I talk with Vinod Subramanian, Chief Data and Product Development Officer at Syapse, about machine learning for healthcare and advancements in cancer treatment. Syapse is a real world evidence company dedicated to improving outcomes for cancer and other serious diseases. Vinod and I talked about the types of healthcare data they work with, the data challenges they encounter, how they validate their models, and how they mitigate bias.
QUOTES:
"Technology is not the answer exemplified of the intent. And the fundamental question, I think, that all of us are confronted by: what is the intent and what in the world that we want to try to help shape?"
"There are infinite possibilities in the terms of patient care with aggregated and harmonized data in healthcare. We all know about the point that data in general is fragmented and decentralized in the industry. Real world data comes from knowledge and knowledge comes from collecting information and of course, information stems from aggregating disparate data. "
"Machine learning today, especially in a life science setting, is leveraged as new ways right to garner new biological insights."
"One of the things that we are also doing is not just about adopting and using (ML and NLP), we strongly believe that we want to share our work. And that would not only raise and mainstream the work of everybody doing it, but also it'll help us in adopting and applying in precision medicine through standards."
"Now not all data is needed equal. When we can improve the way data is collected, connected, analyzed, and consumed, we can not only improve the lives of our community, but it also gives us a way to look at the care continuum very differently."
"There's no guarantee when you get into an initiative which uses machine learning and AI, because it cannot be successful. It has to be a learning experience, but it, there's no guarantee that it will be successful. And there needs to be willingness and appetite to experiment, learn, and iterate, and taking a Socratic approach, and accelerate the journey towards success, anchor down the culture."
LINKS:
Syapse
Vinod Subramanian
QUOTES:
"Technology is not the answer exemplified of the intent. And the fundamental question, I think, that all of us are confronted by: what is the intent and what in the world that we want to try to help shape?"
"There are infinite possibilities in the terms of patient care with aggregated and harmonized data in healthcare. We all know about the point that data in general is fragmented and decentralized in the industry. Real world data comes from knowledge and knowledge comes from collecting information and of course, information stems from aggregating disparate data. "
"Machine learning today, especially in a life science setting, is leveraged as new ways right to garner new biological insights."
"One of the things that we are also doing is not just about adopting and using (ML and NLP), we strongly believe that we want to share our work. And that would not only raise and mainstream the work of everybody doing it, but also it'll help us in adopting and applying in precision medicine through standards."
"Now not all data is needed equal. When we can improve the way data is collected, connected, analyzed, and consumed, we can not only improve the lives of our community, but it also gives us a way to look at the care continuum very differently."
"There's no guarantee when you get into an initiative which uses machine learning and AI, because it cannot be successful. It has to be a learning experience, but it, there's no guarantee that it will be successful. And there needs to be willingness and appetite to experiment, learn, and iterate, and taking a Socratic approach, and accelerate the journey towards success, anchor down the culture."
LINKS:
Syapse
Vinod Subramanian
Resources for Computer Vision Teams:
LinkedIn – Connect with Heather.
Computer Vision Insights Newsletter – A biweekly newsletter to help bring the latest machine learning and computer vision research to applications in people and planetary health.
Computer Vision Strategy Session – Not sure how to advance your computer vision project? Get unstuck with a clear set of next steps. Schedule a 1 hour strategy session now to advance your project.
Foundation Model Assessment – Foundation models are popping up everywhere – do you need one for your proprietary image dataset? Get a clear perspective on whether you can benefit from a domain-specific foundation model.