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:
Taposh Roy leads the innovation team within the Decision Support Group at Kaiser Permanente. His work focuses on journey analytics, deep learning, data science architecture, and strategy. A consumer-focused machine learning and data science geek, Taposh has a unique combination of product, technology and strategy consulting, data science, and startup experience. Previously, he was head of AD products at Inpowered and Netshelter (acquired by Ziff Davis); senior associate consultant in MIT-based consulting company Sapient; and cofounder of biotech company Bio-Integrated Solutions, where he developed DNA sequencers and liquid handling devices for proteomics.
Sabrina Dahlgren is a director in charge of strategic analysis at Kaiser Permanente. Her expertise ranges from statistics and economics to project management and computer science. Sabrina has 20 years’ total work experience in leadership and analytical roles such as vice president of marketing and product development and CRM manager and customer segmentation in technology companies including Vodafone, among others. Sabrina has twice won the Innovation Award at Kaiser, most recently in the category of broadly applicable technology for big data analytics.
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