Pith Number

pith:FFYGGET5

pith:2019:FFYGGET53F6CLMG66B6E2NTE4T

not attested not anchored not stored refs resolved

ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission

Jaan Altosaar, Kexin Huang, Rajesh Ranganath

ClinicalBERT applies bidirectional transformers to clinical notes to outperform baselines in predicting 30-day hospital readmission.

arxiv:1904.05342 v3 · 2019-04-10 · cs.CL · cs.LG

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Record completeness

1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

✓ Portable graph bundle live · download bundle · merged state

The bundle contains the canonical record plus signed events. A mirror can host it anywhere and recompute the same current state with the deterministic merge algorithm.

Claims

C1strongest claim

ClinicalBERT outperforms baselines on 30-day hospital readmission prediction using both discharge summaries and the first few days of notes in the intensive care unit.

C2weakest assumption

That the human evaluation of medical concept relationships and the readmission labels drawn from administrative data are sufficiently reliable proxies for clinical utility and that the model generalizes beyond the training hospital's note style.

C3one line summary

ClinicalBERT applies BERT-style transformers to clinical notes and outperforms baselines on 30-day readmission prediction while revealing human-judged medical concept links.

References

40 extracted · 40 resolved · 3 Pith anchors

[1] Publicly Available Clinical BERT Embeddings 1904 · arXiv:1904.03323

[2] Hospital readmissions in the Medicare population 1984

[3] Aninformatics-based approach to reducing heart failure all-cause readmissions: the Stanfordheartfailuredashboard 2016

[4] Dynamic Hierarchical Clas- sification for Patient Risk-of-Readmission 2015

[5] What’s in a Note? Unpacking Predictive Value in Clinical Note Repre- sentations 2018

Formal links

2 machine-checked theorem links

Cited by

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From Pre-trained Models to Large Language Models: A Comprehensive Survey of AI-Driven Psychological Computing

Receipt and verification

First computed	2026-05-17T23:38:52.903248Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

297063127dd97c25b0def07c4d3664e4c785537d1476358f29a6523f6552b3a1

Aliases

arxiv: 1904.05342 · arxiv_version: 1904.05342v3 · doi: 10.48550/arxiv.1904.05342 · pith_short_12: FFYGGET53F6C · pith_short_16: FFYGGET53F6CLMG6 · pith_short_8: FFYGGET5

Agent API

Resolver JSON Graph JSON Events JSON Schema Signing key

Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/FFYGGET53F6CLMG66B6E2NTE4T \
  | 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: 297063127dd97c25b0def07c4d3664e4c785537d1476358f29a6523f6552b3a1

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "54acfa256804dbd11c7ad213322265ab4c5e5d67bf7aa349334a99b6a3f876fe",
    "cross_cats_sorted": [
      "cs.LG"
    ],
    "license": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
    "primary_cat": "cs.CL",
    "submitted_at": "2019-04-10T17:53:13Z",
    "title_canon_sha256": "07bf6b6c5f4b624787e2712f002aaf6eaa8bdf6905533f64324edb2eb275a6e1"
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  "schema_version": "1.0",
  "source": {
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    "kind": "arxiv",
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  }
}