AutoML and interpretability in healthcare
The healthcare industry requires accuracy and highly interpretable models, but the data is usually plagued by missing information and incorrect values. Enter AutoML and auto-model interpretability. Taposh DuttaRoy and Sabrina Dahlgren discuss tools and strategies for AutoML and interpretability and explain how KP uses them to improve time to develop and deploy highly interpretable models.
| Talk Title | AutoML and interpretability in healthcare |
| Speakers | Taposh DuttaRoy (Kaiser Permanente), Sabrina Dahlgren (Kaiser Permanente) |
| Conference | Strata Data Conference |
| Conf Tag | Big Data Expo |
| Location | San Francisco, California |
| Date | March 26-28, 2019 |
| URL | Talk Page |
| Slides | Talk Slides |
| Video | |
The healthcare industry requires accuracy and highly interpretable models. Depending on the application domain in healthcare (clinical versus operational), the models can range from forecasting to segmentation and clustering to classification and regression, including areas like natural language processing (NLP) and image processing. However, the data is usually plagued by missing information and incorrect values, so data preparation and developing a good model with interpretability are both time-consuming tasks. Enter AutoML and auto-model interpretability. Taposh DuttaRoy and Sabrina Dahlgren discuss tools and strategies for AutoML and interpretability and explain how Kaiser Permanente uses them to improve time to develop and deploy highly interpretable models. Use cases include:
