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1
Introduction
2
SeaKids Hospital
3
ICU Lawson Labs
4
Survey
5
Explainability tool
6
Feature importance
7
Evaluation
8
Representation
9
Bias
10
Representations
11
Trajectory
12
HDP Flow
13
States
14
Model
15
Underlying States
16
Blackbox inference
17
Lifeline pipeline
18
Realtime evaluation
19
Challenges
20
Conclusion
21
Thank you
22
Questions
23
Intervention data
24
Continuous data
25
Two short questions
26
How transferable is this model
27
Feature selection
Description:
Explore a 59-minute conference talk on time series machine learning for healthcare deployment, presented by Anna Goldenberg at the EWSC-MIT EECS Joint Colloquium Series. Delve into the application of ML in healthcare settings, including ICU environments and pediatric hospitals. Learn about explainability tools, feature importance, evaluation methods, and representation challenges in medical AI. Discover innovative approaches like HDP Flow States and the Lifeline pipeline for real-time evaluation. Gain insights into model transferability, feature selection, and the use of intervention and continuous data in healthcare ML. Engage with the pressing biomedical questions and foundational machine learning advances discussed in this collaborative series between the Eric and Wendy Schmidt Center at the Broad Institute and AI+D within MIT's EECS Department.

Time Series Machine Learning for Deployment in Healthcare

Broad Institute
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