ISPOR Task Force Indicates Five Areas Machine Learning Could Support Health Economics and Outcomes Research
Earlier this month, ISPOR released a report in the journal Value in Health detailing good practices in using machine learning for health economics and outcomes research (HEOR). According to the lead authors, machine learning can enable deep analysis of complex datasets, identifying trends that may otherwise go unnoticed. To enable the best possible use of the technology in HEOR, they identified these key areas that could benefit the field.
Amazon Teams Up with Thread to Advance Decentralized Clinical Trials Platform
Amazon Web Services has announced a new partnership with the US-based tech company Thread to promote and expand a digital [...]
Machine Learning Offers Insights on COVID-19 ICU Utilization
The influx of COVID-19 patients pushed intensive care units (ICUs) to the breaking point in early 2021. Triaging who would [...]
Value in Health Study: Evaluating Machine Learning-based Risk Models with Real World Data
Several healthcare fields have taken advantage of machine learning-based approaches in recent years. In a recent Value in Health article, [...]
Stanford Center for Health Education Launches Online Program in AI and Healthcare.
A new program launched by the Stanford Center for Health Education aims to advance the delivery of patient care and [...]
