Author

Amir Rafe

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Preprint Sep 2026

Counting the Uncounted: Population-Level Surveillance of Documented Pregnancy and Fetal Harm in Police Crash Narratives with a System One Model (Jev)

Motor-vehicle crashes are a leading cause of traumatic fetal death in the United States, yet no crash database records pregnancy. Existing estimates come from national investigation samples with wide intervals, trauma registries seeing only the injured, or record linkage in a few states. Police narratives record pregna...

Amir Rafe, Subasish Das · 0 citations

Preprint Sep 2026

CHOIR: heterogeneity-aware conformal prediction for crash injury severity across driver safety strata

Transportation agencies increasingly predict crash-injury severity with statistical and machine-learning models, but these models do not state how often their output contains the recorded injury level or for which groups of drivers it fails, a gap that matters most for motorcyclists and unrestrained drivers. This study...

Amir Rafe, Subasish Das · 0 citations

#machine learning Preprint Sep 2026

A distribution-free certification framework for trustworthy crash-severity prediction

Crash-severity models inform screening, dispatch and site prioritization, yet are deployed without a finite-sample statement of what one prediction means. Off-the-shelf guarantees fail here, because the features that make crash severity distinctive defeat them: the KABCO outcome is ordinal, the recorded label is a fiel...

Amir Rafe, Subasish Das · 0 citations