In a Feb. 25 news release, the university said Temporal Learning Lite, or TL-Lite, is a visualization and forecasting tool hoping to bridge the gap between machine learning analysis and clinical visualization.
“While the individual elements of this tool are well known, their integration into an interactive clinical research tool is new and useful for health professionals,” said study author Jeremy Weiss, PhD, assistant professor of health informatics at CMU’s Heinz College. “With familiarization, users can conduct preliminary analyses in minutes.”
The study appears in Proceedings of Machine Learning Research and demonstrates the model using EHRs pertaining to three health matters.
The research was funded by Carnegie Mellon University, Amazon Web Services and Microsoft Azure.
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