Signal Fidelity Index-aware calibration for addressing distributional shift in predictive modeling across heterogeneous real-world data
Jingya Cheng, Jiazi Tian, Federica Spoto, Alaleh Azhir, Daniel Mork, Hossein Estiri · 2025
Read Signal Fidelity Index-aware calibration for addressing distributional shift in predictive modeling across heterogeneous real-world data by Jingya Cheng, Jiazi Tian, Federica Spoto, Alaleh Azhir, Daniel Mork, Hossein Estiri (2025) in the Tower of Babel, a free online library of public-domain works preserved in clean, searchable text. No sign-up. The full text is served as a plain-text file at a predictable URL, where text is present: https://antarctic-labs.com/tower-of-babel/pmc-12823559.txt.
Description
Machine learning models trained on real-world data (RWD) often experience performance degradation when deployed across different settings due to distributional shift. However, a fundamental but under-explored factor contributing to this degradation is the decay of diagnostic signals: systematic variability in diagnostic quality and consistency across institutional contexts, which affects the reliability of clinical codes used for model training and prediction.
Catalog record
| CREATOR | Jingya Cheng, Jiazi Tian, Federica Spoto, Alaleh Azhir, Daniel Mork, Hossein Estiri |
|---|---|
| YEAR | 2025 |
| COLLECTION | PAPERS |
| FORMAT | TXT |
| FILE SIZE | 91 KB |
| VERSION | 1.0 |
| LICENSE | CC BY 4.0 |
| RIGHTS | CC BY |
| SOURCE | Europe PMC |