The Hard Part of Machine Learning with Lynn Langit
What are the hard parts of machine learning? Richard chats with Lynn Langit about her work helping the Mayo Clinic improve patient outcomes using machine learning to understand patient data better. Lynn talks about the challenges of multi-modal data analytics - taking all the different data collected from a patient, like an X-ray or video, along with treatment notes, to create an overall picture of treatment and outcome. Then multiply that by thousands of patients, making a complicated data problem with huge challenges in testing and validation. How do you know that the machine learning model is correct? The key to practical machine learning is in the fundamentals - working on each step before you jump to the more complex goals!
Episode 938 of the RunAs Radio podcast, hosted by Lynn Langit, Richard Campbell, titled "The Hard Part of Machine Learning with Lynn Langit" was published on June 26, 2024 and runs 35 minutes.
June 26, 2024 ·35m · RunAs Radio
Summary
What are the hard parts of machine learning? Richard chats with Lynn Langit about her work helping the Mayo Clinic improve patient outcomes using machine learning to understand patient data better. Lynn talks about the challenges of multi-modal data analytics - taking all the different data collected from a patient, like an X-ray or video, along with treatment notes, to create an overall picture of treatment and outcome. Then multiply that by thousands of patients, making a complicated data problem with huge challenges in testing and validation. How do you know that the machine learning model is correct? The key to practical machine learning is in the fundamentals - working on each step before you jump to the more complex goals!
Episode Description
What are the hard parts of machine learning? Richard chats with Lynn Langit about her work helping the Mayo Clinic improve patient outcomes using machine learning to understand patient data better. Lynn talks about the challenges of multi-modal data analytics - taking all the different data collected from a patient, like an X-ray or video, along with treatment notes, to create an overall picture of treatment and outcome. Then multiply that by thousands of patients, making a complicated data problem with huge challenges in testing and validation. How do you know that the machine learning model is correct? The key to practical machine learning is in the fundamentals - working on each step before you jump to the more complex goals!
Links
- Lynn on GitHub
- BiomedCLIP
- Evaluation Metrics and Statistical Tests for Machine Learning
- GitHub Copilot Workspace
- Gemini in BigQuery
- Basic Bioinformatics for IT
- HistoGPT
Recorded May 17, 2024
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