Uncertainty Quantification for Clinical Outcome Predictions with (Large) Language Models (2025.findings-naacl)
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| Challenge: | Language models (LMs) have significant potential for clinical prediction tasks . however, unreliable decisions can result in significant costs due to compromised patient safety and ethical concerns . |
| Approach: | They propose to combine ensembling and multi-tasking approaches to reduce uncertainty in EHRs by using multi-tapping methods. |
| Outcome: | The proposed framework reduces model uncertainty in white-box and black-box settings, and improves model transparency in both settings. |
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