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ExperienceAI – A Machine Learning Conference by Univ.AI
The first hugely successful in-person Univ.AI conference held in pre-covid times with a fully capacity audience in Bangalore. Featuring global artificial intelligence and data science professionals and faculty from Harvard, UCLA, IISC, Caltech, PhonePe and Microsoft.
Machine Learning for Biology and Medicine
– Sriram Sankararaman, UCLA
In the first part of my talk, I will focus on machine learning problems arising in the field of genomics. The enormous cost savings in genome sequencing and the availability of genetic variation data from millions of individuals have opened the possibility of using genetic information to identify the cause of diseases, develop effective drugs, predict disease risks and personalize treatment. While genome-wide association studies provide a powerful paradigm for discovering disease-causing genes, the hidden genetic structure of human populations can confound these studies. I will describe statistical models that can infer this hidden structure and show how these inferences lead to new insights into the genetic basis of diseases.
In the second part of my talk, I will discuss how the availability of large-scale electronic health records opens up the possibility of using machine learning in clinical settings. Using electronic records from approximately 60,000 operations over five years at UCLA Hospital, I will describe efforts to use machine learning algorithms to predict mortality after surgery. Our results show that these algorithms can accurately predict mortality based on information available before surgery. This indicates that automated predictive systems have great potential to improve clinical care.
Topics – Machine Learning, AI, Artificial Intelligence, Reinforcement Learning, Deep Learning, Computational Medicine
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