CAREER: Temporal Learning Towards Trustworthy Decision for Healthcare — NSF Award to University of Memphis (TN, $500,000)
In healthcare machine learning (ML) models, the data characteristics can change over time. These models are trained on existing and historical data but are intended to be applied to future, unseen data for prediction. Temporal shifts in data, labels, and patient populations may undermine confidence in the use of ML mod
| Award title | CAREER: Temporal Learning Towards Trustworthy Decision for Healthcare |
|---|---|
| Award ID | 2440381 |
| Awardee | University of Memphis |
| City | MEMPHIS |
| State | TN |
| Amount obligated | $500,000 |
| Principal investigator | Xiaolei Huang |
| Program | Info Integration & Informatics |
| Start date | 09/01/2025 |
| Abstract | In healthcare machine learning (ML) models, the data characteristics can change over time. These models are trained on existing and historical data but are intended to be applied to future, unseen data for prediction. Temporal shifts in data, labels, and patient populations may undermine confidence in the use of ML models utilization and raise concerns about their trustworthiness. Importantly, patient patterns can shift implicitly and may require extra inference from clinical notes (e.g., notes |
| Source | NSF Awards |
$799/mo
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