pith:LBWCSO2C
An Empirical Study of Machine Learning Robustness and Scalability for Imbalanced Tabular Clinical Data in Emergency and Critical Care
Tabular foundation models achieve competitive results on imbalanced clinical data at lower computational cost than deeper alternatives.
arxiv:2512.21602 v3 · 2025-12-25 · cs.LG · cs.CV
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\usepackage{pith}
\pithnumber{LBWCSO2CJZFXXXIWF6KHLSEM74}
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Record completeness
Claims
Tabular foundation models showed promise by combining competitive performance at low computational cost.
That the two chosen datasets and seven prediction tasks sufficiently represent real-world clinical imbalance scenarios and that results will generalize beyond MIMIC-IV-ED and eICU.
Tabular foundation models achieve competitive weighted F1 scores on imbalanced emergency care data while scaling more efficiently than some deep models like TabNet.
Receipt and verification
| First computed | 2026-05-27T01:04:54.194076Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
586c293b424e4b7bdd162f9475c88cff2bfb422b7a507f8e098172cc65ab2d9b
Aliases
· · · · ·Agent API
Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/LBWCSO2CJZFXXXIWF6KHLSEM74 \
| jq -c '.canonical_record' \
| python3 -c "import sys,json,hashlib; b=json.dumps(json.loads(sys.stdin.read()), sort_keys=True, separators=(',',':'), ensure_ascii=False).encode(); print(hashlib.sha256(b).hexdigest())"
# expect: 586c293b424e4b7bdd162f9475c88cff2bfb422b7a507f8e098172cc65ab2d9b
Canonical record JSON
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"license": "http://creativecommons.org/licenses/by-sa/4.0/",
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