Electronic Health Records

In the Google Blog:

Deep Learning for Electronic Health Records
Posted by Alvin Rajkomar MD, Research Scientist and Eyal Oren PhD, Product Manager, Google AI

When patients get admitted to a hospital, they have many questions about what will happen next. When will I be able to go home? Will I get better? Will I have to come back to the hospital? Having precise answers to those questions helps doctors and nurses make care better, safer, and faster — if a patient’s health is deteriorating, doctors could be sent proactively to act before things get worse.

Predicting what will happen next is a natural application of machine learning. We wondered if the same types of machine learning that predict traffic during your commute or the next word in a translation from English to Spanish could be used for clinical predictions. For predictions to be useful in practice they should be, at least:

Scalable: Predictions should be straightforward to create for any important outcome and for different hospital systems. Since healthcare data is very complicated and requires much data wrangling, this requirement is not straightforward to satisfy.

Accurate: Predictions should alert clinicians to problems but not distract them with false alarms. With the widespread adoption of electronic health records, we set out to use that data to create more accurate prediction models. ... "