Pith Number

pith:4ORCVRK6

pith:2026:4ORCVRK62JFHRMPJHF5SJGNBMO

not attested not anchored not stored refs resolved

Dynamical Predictive Modelling of Cardiovascular Disease Progression Post-Myocardial Infarction via ECG-Trained Artificial Intelligence Model

Adelaide de Vecchi, Andrew King, Lupo Lovatelli, Oleg Aslanidi, Riccardo Cavarra, Shaheim Ogbomo-Harmitt, Shahid Aziz

Pretraining ECG models with patient-specific temporal contrastive learning raises post-MI outcome prediction AUC from 0.608 to 0.794 in small-data settings.

arxiv:2605.13568 v1 · 2026-05-13 · cs.LG · cs.AI

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\pithnumber{4ORCVRK62JFHRMPJHF5SJGNBMO}

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1 Bitcoin timestamp

2 Internet Archive

3 Author claim open · sign in to claim

4 Citations open

5 Replications open

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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

The proposed model outperformed a model trained from scratch (0.794 vs 0.608 AUC) showing that clinically structured ECG modelling improves classification in limited data regimes.

C2weakest assumption

That the contrastive pretraining objective with patient-specific temporal information extracts features that are genuinely predictive of post-MI clinical outcomes rather than dataset-specific artifacts.

C3one line summary

A contrastive-learning ECG foundation model with multitask heads predicts post-MI outcomes better than training from scratch (AUC 0.794 vs 0.608).

References

9 extracted · 9 resolved · 1 Pith anchors

[1] Digital Twins for Predictive Modelling of Thrombosis and Stroke Risk: Current Approaches and Future Directions, 2026 · doi:10.1055/a-2761-

[2] 2023 ESC Guidelines for the management of acute coronary syndromes, 2023 · doi:10.1093/eurheartj/ehad191

[3] Explainable machine learning models to improve prediction of incident stroke in atrial fibrillation patients using health records, medical imaging and ECG derived metrics, 2025 · doi:10.1093/eurheartj/ehaf784.4422

[4] CLECG: A Novel Contrastive Learning Framework for Electrocardiogram Arrhythmia Classification, 1993 · doi:10.1109/lsp.2021.3114119

[5] A Simple Framework for Contrastive Learning of Visual Representations 2020 · doi:10.48550/arxiv.2002.05709

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-18T02:44:23.399751Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

e3a22ac55ed24a78b1e9397b2499a163b61926a675ae8a584813f52ee967d6ba

Aliases

arxiv: 2605.13568 · arxiv_version: 2605.13568v1 · doi: 10.48550/arxiv.2605.13568 · pith_short_12: 4ORCVRK62JFH · pith_short_16: 4ORCVRK62JFHRMPJ · pith_short_8: 4ORCVRK6

Agent API

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Verify this Pith Number yourself

curl -sH 'Accept: application/ld+json' https://pith.science/pith/4ORCVRK62JFHRMPJHF5SJGNBMO \
  | 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: e3a22ac55ed24a78b1e9397b2499a163b61926a675ae8a584813f52ee967d6ba

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "d944f0a58a486c868682e73440da013e5e5476dac956c1e8397c783904a81baa",
    "cross_cats_sorted": [
      "cs.AI"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-13T14:05:43Z",
    "title_canon_sha256": "aa011982f97052cea94041a938caa532bd86dc1c6e4c2478a72944d1f702054f"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.13568",
    "kind": "arxiv",
    "version": 1
  }
}