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Knowledge Uncertainty Estimation for Reliable Clinical Decision Support: A Delirium Risk Prognosis Case Study.
Adrian Lindenmeyer1, Sai Veeranki2,3, Stefan Franke1
1Innovation Center Computer Assisted Surgery (ICCAS), Leipzig University, Leipzig, Germany.
Studies in Health Technology and Informatics
|April 24, 2025
Summary
Spectral Normalized Neural Gaussian Processes (SNGP) and Ensemble Neural Networks (ENN) estimate knowledge uncertainty to improve trust in AI for delirium risk prediction. SNGP showed superior performance in detecting unfamiliar data compared to ENN.
Area of Science:
- Artificial Intelligence in Healthcare
- Machine Learning for Clinical Decision Support
- Uncertainty Quantification in Medical AI
Background:
- Clinical adoption of predictive models is limited by a lack of trust, particularly when models encounter unfamiliar data.
- Estimating knowledge uncertainty (KU) is crucial for enhancing the reliability of AI in healthcare settings.
- This study focuses on delirium risk prediction, a critical area for improving patient outcomes.
Purpose of the Study:
- To evaluate Ensemble Neural Networks (ENN) and Spectral Normalized Neural Gaussian Processes (SNGP) for quantifying knowledge uncertainty (KU).
- To assess the capability of ENN and SNGP in detecting out-of-distribution (OoD) data in delirium risk prediction.
- To compare the performance of ENN and SNGP against a Random Forest (RF) baseline.
Main Methods:
- A cohort of hospitalized patients was used to train and test predictive models.
- Delirium risk was predicted using ENN, SNGP, and a Random Forest (RF) baseline.
- Out-of-distribution (OoD) data was synthetically generated using feature randomization and swapping techniques to test model robustness.
Main Results:
- Both ENN and SNGP achieved high performance in delirium risk prediction (AUROC ~0.90), comparable to the RF baseline.
- SNGP demonstrated superior performance in detecting out-of-distribution (OoD) data, correctly identifying 82.4% and 92.2% of OoD samples in different scenarios.
- ENN also showed improved OoD detection over the baseline, identifying 68.8% and 86.0% of OoD samples.
Conclusions:
- All evaluated models effectively predicted delirium risk.
- Spectral Normalized Neural Gaussian Processes (SNGP) exhibited superior capability in identifying out-of-distribution data.
- SNGP's robust knowledge uncertainty estimation holds significant potential for increasing the trustworthiness of clinical decision support systems.
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