{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:E2QRSILGZGMEVRBDAXPFFFY63F","short_pith_number":"pith:E2QRSILG","schema_version":"1.0","canonical_sha256":"26a1192166c9984ac42305de52971ed9737b79003ac084e884d8f185e38d82a7","source":{"kind":"arxiv","id":"2607.23412","version":1},"attestation_state":"computed","paper":{"title":"Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Abram Hindle, Anita Khalafbeigi, Jie Lin, Padma Kaul, Russell Greiner, Sunil V. Kalmady, Weijie Sun","submitted_at":"2026-07-26T02:04:13Z","abstract_excerpt":"Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences. We benchmark cross-dataset generalization on three tasks - heart failure classification, 30-day all-cause mortality, and 30-day mortality among sinus-rhythm ECGs - using two large cohorts (MIMIC-IV and the Alberta Cohort). To reduce vendor-specific measurement mismatch, we build a harmonized, interpretable feature representation computed directly from raw waveforms: FeatureDB morphology/heart-rate-variabil"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2607.23412","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2026-07-26T02:04:13Z","cross_cats_sorted":[],"title_canon_sha256":"e21d390411518ac02698559ba238705c7c90e1c75b2f9966f7ffda80bfc93904","abstract_canon_sha256":"86bfc3fa59ba57cc91d81b9f3c6a4b796549ae5c3e9158b7ed3895febb87f41e"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-28T01:22:51.741231Z","signature_b64":"6imuJkz4+CbC5NmED8ABJLAY4Az5P9a+zJBdJDbSjl6pg3n9XhhHLXPuikzVp9jwoXGp89TNh4odlhq/YtM4CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"26a1192166c9984ac42305de52971ed9737b79003ac084e884d8f185e38d82a7","last_reissued_at":"2026-07-28T01:22:51.740441Z","signature_status":"signed_v1","first_computed_at":"2026-07-28T01:22:51.740441Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Harmonized Interpretable ECG Waveform Features for Robust Cross-Dataset Clinical Prediction","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Abram Hindle, Anita Khalafbeigi, Jie Lin, Padma Kaul, Russell Greiner, Sunil V. Kalmady, Weijie Sun","submitted_at":"2026-07-26T02:04:13Z","abstract_excerpt":"Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences. We benchmark cross-dataset generalization on three tasks - heart failure classification, 30-day all-cause mortality, and 30-day mortality among sinus-rhythm ECGs - using two large cohorts (MIMIC-IV and the Alberta Cohort). To reduce vendor-specific measurement mismatch, we build a harmonized, interpretable feature representation computed directly from raw waveforms: FeatureDB morphology/heart-rate-variabil"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.23412","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2607.23412/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2607.23412","created_at":"2026-07-28T01:22:51.740850+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.23412v1","created_at":"2026-07-28T01:22:51.740850+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.23412","created_at":"2026-07-28T01:22:51.740850+00:00"},{"alias_kind":"pith_short_12","alias_value":"E2QRSILGZGME","created_at":"2026-07-28T01:22:51.740850+00:00"},{"alias_kind":"pith_short_16","alias_value":"E2QRSILGZGMEVRBD","created_at":"2026-07-28T01:22:51.740850+00:00"},{"alias_kind":"pith_short_8","alias_value":"E2QRSILG","created_at":"2026-07-28T01:22:51.740850+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F","json":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F.json","graph_json":"https://pith.science/api/pith-number/E2QRSILGZGMEVRBDAXPFFFY63F/graph.json","events_json":"https://pith.science/api/pith-number/E2QRSILGZGMEVRBDAXPFFFY63F/events.json","paper":"https://pith.science/paper/E2QRSILG"},"agent_actions":{"view_html":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F","download_json":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F.json","view_paper":"https://pith.science/paper/E2QRSILG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.23412&json=true","fetch_graph":"https://pith.science/api/pith-number/E2QRSILGZGMEVRBDAXPFFFY63F/graph.json","fetch_events":"https://pith.science/api/pith-number/E2QRSILGZGMEVRBDAXPFFFY63F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F/action/storage_attestation","attest_author":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F/action/author_attestation","sign_citation":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F/action/citation_signature","submit_replication":"https://pith.science/pith/E2QRSILGZGMEVRBDAXPFFFY63F/action/replication_record"}},"created_at":"2026-07-28T01:22:51.740850+00:00","updated_at":"2026-07-28T01:22:51.740850+00:00"}