pith:542ZKGNW
Capture Timing-Attention of Events in Clinical Time Series
LITT aligns clinical events on a virtual relative timeline to focus attention on their timing for personalized predictions.
arxiv:2602.10385 v4 · 2026-02-11 · cs.LG · cs.AI
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Claims
LITT enables temporary alignment of sequential events on a virtual relative timeline, thereby enabling event-timing-focused attention and personalized interpretations of clinical trajectories, validated on real-world longitudinal EHR data from 3,276 breast cancer patients to predict the onset timing of cardiotoxicity-induced heart disease.
That treating timing as a computable dimension via relative timestamps on a virtual timeline captures meaningful alignments and causal patterns without introducing artifacts or losing information from the original observed times.
LITT aligns individual clinical event sequences on a relative timeline to enable timing-aware attention and better prediction of personalized health trajectories.
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Receipt and verification
| First computed | 2026-05-28T02:04:46.132164Z |
|---|---|
| Builder | pith-number-builder-2026-05-17-v1 |
| Signature | Pith Ed25519
(pith-v1-2026-05) · public key |
| Schema | pith-number/v1.0 |
Canonical hash
ef359519b6217df18e68a22de057795324d3ce2268f80f9eb2f31b5be768156b
Aliases
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Verify this Pith Number yourself
curl -sH 'Accept: application/ld+json' https://pith.science/pith/542ZKGNWEF67DDTIUIW6AV3ZKM \
| 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: ef359519b6217df18e68a22de057795324d3ce2268f80f9eb2f31b5be768156b
Canonical record JSON
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