Transparent, Reproducible AI Tool for Health Information Analysis
Written by AZoRoboticsFeb 26 2021
Clinical research necessitates the mining of data to gain crucial insights. Machine learning, which involves developing algorithms to identify patterns, finds it hard to do this in the case of data related to health records since information of this kind is neither static nor collected regularly.
Prof. Jeremy Weiss, an expert in machine learning and healthcare informatics. Image Credit: Carnegie Melon University Heinz College.
As part of a new study, researchers have developed a transparent, reproducible machine learning tool to allow the analysis of health information. The tool can be employed in clinical forecasting, which can estimate trends and outcomes in individual patients.
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Supporting Responsible Use Of AI And Equitable Outcomes In Financial Services â Federal Reserve Governor Lael Brainard At The AI Academic Symposium Hosted By The Board Of Governors Of The Federal Reserve System, Washington, D.C. (Virtual Event) Date
12/01/2021
Today s symposium on the use of artificial intelligence (AI) in financial services is part of the Federal Reserve s broader effort to understand AI s application to financial services, assess methods for managing risks arising from this technology, and determine where banking regulators can support responsible use of AI and equitable outcomes by improving supervisory clarity.1
The potential scope of AI applications is wide ranging. For instance, researchers are turning to AI to help analyze climate change, one of the central challenges of our time. With nonlinearities and tipping points, climate change is highly complex, and quantification for risk assessments requires the analysis of vast amounts of data, a task