CPH co-director Ida Sim, Berkeley Institute for Data Science director Fernando Pérez, and colleagues have launched Agile Metabolic Health--and a new paradigm for health algorithm development and diabetes management. The inaugural effort of the Open Platforms for Powering Science and Society initiative at the UC Berkeley College of Computing, Data Science, and Society, Agile Metabolic Health will pilot a new approach to the development of algorithms and clinical decision support using data from wearable devices, such as blood pressure cuffs and continuous glucose monitoring.

Sergio Baranzini describes how use of artificial intelligence and machine learning models–coupled with biomedical knowledge graphs–can identify subtle yet potentially significant signs and symptoms in the medical record, improving diagnosis of MS and other conditions.

Adam Yala talks machine learning, cancer risk prediction and the future transformation of medicine with The Brain Surgeon’s Take Podcast.

Which questions does machine learning answer well or badly? ML models have lacked the ability to label confidence intervals or quantify statistical certainty when producing results. Michael Jordan and colleagues introduce “prediction-powered inference,” a standardized protocol for constructing valid confidence intervals and P values to support responsible and reliable inference.

A. Jay Holmgren and colleagues find time by physicians in the Electronic Medical Record is up sharply, both during and after patient scheduled hours. UCSF providers spent nearly 5.5 of 8 patient scheduled hours working in the EMR. Health systems and policymakers may need to adjust both productivity expectations and reimbursement policies.

Jonathan Kolstad and Berkeley Haas colleagues have created Healthpilot–a free, online platform that uses machine learning to weigh complex personalized factors and provide Medicare consumers with recommendations for better, and often lower cost, coverage options than those offered by insurance brokers.