Pith Number

pith:3DFFD73L

pith:2026:3DFFD73LNAG5VH23IBXOK56MAA

not attested not anchored not stored refs resolved

PhysioSeq2Seq: A Hybrid Physiological Digital Twin and Sequence-to-Sequence LSTM for Long-Horizon Glucose Forecasting in Type 1 Diabetes

Clara Mosquera-Lopez, Lizhong Chen, Neville Mehta, Peter G. Jacobs, Phat Tran, Robert H. Dodier

PhysioSeq2Seq reduces long-horizon glucose forecast bias by injecting patient-matched physiological states into a sequence-to-sequence LSTM.

arxiv:2605.16860 v1 · 2026-05-16 · cs.LG · cs.AI · q-bio.QM

Open paper page JSON Open Graph Bundle Merged state Verified badge What is a Pith Number?

Add to your LaTeX paper

\usepackage{pith}
\pithnumber{3DFFD73LNAG5VH23IBXOK56MAA}

Prints a linked badge after your title and injects PDF metadata. Compiles on arXiv. Learn more · Embed verified badge

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

At the 240-minute horizon, PhysioSeq2Seq achieves a mean absolute error of 39.28 mg/dL and a mean error of -10.62 mg/dL, reducing bias by 13.89 mg/dL over the recursive LSTM and reducing mean absolute error by 28.62 mg/dL over the ODE-based digital twin.

C2weakest assumption

That selecting one of 300 pre-parameterized digital twins solely from a 3-hour CGM segment supplies internal ODE states accurate enough to constrain the LSTM's long-horizon output without introducing new systematic errors or selection bias.

C3one line summary

Hybrid digital-twin matching plus Seq2Seq LSTM reduces 240-minute glucose forecast bias by 13.89 mg/dL and MAE by 28.62 mg/dL versus baselines on held-out T1DEXI data.

References

26 extracted · 26 resolved · 1 Pith anchors

[1] and Haueter, Ulrich and Massi-Benedetti, Massimo and Federici, Marco Orsini and Pieber, Thomas R · doi:10.1088/0967-3334/25/4/010

[2] and Wang, Fei and Haynes, Aveni and Gregory, Gabriel A

[3] Diabetes , volume = 2011

[4] Breton and Sriram Sankaranarayanan , journal = 2020

[5] 2020 , volume = 2020

Formal links

2 machine-checked theorem links

Receipt and verification

First computed	2026-05-20T00:03:26.769148Z
Builder	pith-number-builder-2026-05-17-v1
Signature	Pith Ed25519 (`pith-v1-2026-05`) · public key
Schema	pith-number/v1.0

Canonical hash

d8ca51ff6b680dda9f5b406ee577cc0031020393454ba3ebe94adf4a0f3f76e9

Aliases

arxiv: 2605.16860 · arxiv_version: 2605.16860v1 · doi: 10.48550/arxiv.2605.16860 · pith_short_12: 3DFFD73LNAG5 · pith_short_16: 3DFFD73LNAG5VH23 · pith_short_8: 3DFFD73L

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/3DFFD73LNAG5VH23IBXOK56MAA \
  | 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: d8ca51ff6b680dda9f5b406ee577cc0031020393454ba3ebe94adf4a0f3f76e9

Canonical record JSON

{
  "metadata": {
    "abstract_canon_sha256": "4406ead7226486b8f012d3ca519ec88cfd3330b9097162e6181607749c2b8b4f",
    "cross_cats_sorted": [
      "cs.AI",
      "q-bio.QM"
    ],
    "license": "http://creativecommons.org/licenses/by/4.0/",
    "primary_cat": "cs.LG",
    "submitted_at": "2026-05-16T07:50:00Z",
    "title_canon_sha256": "9835345f5184346d40708307bc34b41b06ff56aa377315e2671d80b5a09e3680"
  },
  "schema_version": "1.0",
  "source": {
    "id": "2605.16860",
    "kind": "arxiv",
    "version": 1
  }
}