How machine learning is saving lives while saving hospitals money

Researchers at the University of Washington, Tacoma, have built a machine learning system capable of predicting readmission risks for congestive heart failure patients. It has shown good results in a pilot deployment, and now the team hopes to commercialize the technology.

Apixio raises $13.5M to bring NLP to health care

Apixio has raised a $13.5 million series C round of venture capital from Bain Capital Ventures and several angel investors. The company’s technology extracts data from numerous types of medical records, including patient charts and doctors’ notes, in order to help hospitals and insurance companies get a better sense of patients’ health for risk-assessment purposes. Apixio has raised more than $22 million since launching in 2009. It first launched publicly in 2011, then focusing on its semantic-analysis engine for identifying latent connections between diagnoses and symptoms.

New project lets Watson actually work with doctors

IBM has shared some details about a new project called WatsonPaths that lets doctors actually interact with the system to understand how it came to its conclusions, and to tell it whether its “thinking” was right. This type of interaction is critical in any type of machine learning system where speed isn’t the primary objective, because it lets humans see things they might not have and also train the machines to be more accurate in the future. WatsonPaths is a GUI-based tool and is being developed along with doctors at the Cleveland Clinic.