Division of Health Sciences Informatics Grand Rounds: Luis Ahumada

Description

Luis Ahumada, director of predictive analytics at Johns Hopkins All Children's Hospital and assistant professor of anesthesiology and critical care medicine at the Johns Hopkins University School of Medicine, will give a talk entitled "Emerging Machine Learning Methods and the Future of Machine Learning in Healthcare" for the [Division of Health Sciences Informatics](Division of Health Sciences Informatics) Grand Rounds.

Please join the talk via webcast.

Ahumada is also co-director of the Center for Pediatric Data Science and Analytic Methodology at the Johns Hopkins All Children's Hospital and was the chief data science manger for the Enterprise Advanced Analytics department at The Children's Hospital of Philadelphia. His primary area of interest is the research and development of practical and novel solutions toward the mining of large datasets, the design of information visualization tools, and implementation of predictive models based on electronic health record systems, with the aim to implement practical clinical decision support applications. Since first working at GlaxoSmithKline in the Knowledge Discovery in Databases group as a computer scientist, he has successfully helped with the design, implementation, and research of data science projects, which includes predictive and classification models using electronic medical records and data streams.

Learning objectives:

1. Describe current machine learning methods
2. Describe target use cases in healthcare for the use of machine learning
3. Articulate benefits and risks of machine learning that has been used 4.Describe where we think machine learning in health care is going in the short term and in the long term