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Lessons Learned from Developing ML for Healthcare

Tuesday, December 10, 2019

Posted by Yun Liu, Research Scientist and Po-Hsuan Cameron Chen, Research Engineer, Google HealthTIMI Risk Scorea variety of medical applicationscomplex medical recordsimproving the accuracy of genomic sequencingcardiovascular risk factorsrefractive errorgrading diabetic eye diseasediagnosing metastatic breast cancerleverage ML-based tools in an interactive fashionNature MaterialsJournal of the American Medical AssociationJAMAHow to Develop Machine Learning Models for HealthcareHow to develop machine learning models for healthcarepdfNature Materials

Two examples of the translational process of developing, validating, and implementing ML models for healthcare based on our work in detecting diabetic eye disease and metastatic breast cancer.

Empowering Doctors to Better Understand Machine Learning for HealthcareUsers’ Guide to the Medical Literature: How to Read Articles that use Machine LearningJAMAmemorize the training dataset

The intersection of two fields: ML and healthcare creates ambiguity in the term “validation dataset”. An ML validation set is typically used to refer to the dataset used for hyperparameter tuning, whereas a “clinical” validation set is typically used for final evaluation. To reduce confusion, we have opted to refer to the (ML) validation set as the “tuning” set.

Future outlookAcknowledgementsKey contributors to these projects include Yun Liu, Po-Hsuan Cameron Chen, Jonathan Krause, and Lily Peng. The authors would like to acknowledge Greg Corrado and Avinash Varadarajan for their advice, and the Google Health team for their support.