{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:KU5QAKDVTQMQENJRBPXSDZ3U5R","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"714d70de83b69fa09a9ad07b88279876fcfd784107cdeb70feab9e167974dde7","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2019-05-27T16:39:17Z","title_canon_sha256":"da65be4959619602a14dc71133dfd2e671cc8e8f09cad4691502c124935b4f2d"},"schema_version":"1.0","source":{"id":"1905.11333","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1905.11333","created_at":"2026-07-04T23:59:32Z"},{"alias_kind":"arxiv_version","alias_value":"1905.11333v3","created_at":"2026-07-04T23:59:32Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.11333","created_at":"2026-07-04T23:59:32Z"},{"alias_kind":"pith_short_12","alias_value":"KU5QAKDVTQMQ","created_at":"2026-07-04T23:59:32Z"},{"alias_kind":"pith_short_16","alias_value":"KU5QAKDVTQMQENJR","created_at":"2026-07-04T23:59:32Z"},{"alias_kind":"pith_short_8","alias_value":"KU5QAKDV","created_at":"2026-07-04T23:59:32Z"}],"graph_snapshots":[{"event_id":"sha256:142be75cd4ac04708747a62ecd8d161c852b6ce676b9bc49279eb71aa61c0373","target":"graph","created_at":"2026-07-04T23:59:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/1905.11333/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Electrocardiography (ECG) signals are commonly used to diagnose various cardiac abnormalities. Recently, deep learning models showed initial success on modeling ECG data, however they are mostly black-box, thus lack interpretability needed for clinical usage. In this work, we propose MultIlevel kNowledge-guided Attention networks (MINA) that predict heart diseases from ECG signals with intuitive explanation aligned with medical knowledge. By extracting multilevel (beat-, rhythm- and frequency-level) domain knowledge features separately, MINA combines the medical knowledge and ECG data via a mu","authors_text":"Cao Xiao, Hongyan Li, Jimeng Sun, Shenda Hong, Tengfei Ma","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2019-05-27T16:39:17Z","title":"MINA: Multilevel Knowledge-Guided Attention for Modeling Electrocardiography Signals"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.11333","kind":"arxiv","version":3},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:90e34415248df084001cafe6f0c8b5db1738e722fe07a2734af4b72e97a4bf4b","target":"record","created_at":"2026-07-04T23:59:32Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"714d70de83b69fa09a9ad07b88279876fcfd784107cdeb70feab9e167974dde7","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.SP","submitted_at":"2019-05-27T16:39:17Z","title_canon_sha256":"da65be4959619602a14dc71133dfd2e671cc8e8f09cad4691502c124935b4f2d"},"schema_version":"1.0","source":{"id":"1905.11333","kind":"arxiv","version":3}},"canonical_sha256":"553b0028759c190235310bef21e774ec791586f197ac89512c7102c4a657cc98","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"553b0028759c190235310bef21e774ec791586f197ac89512c7102c4a657cc98","first_computed_at":"2026-07-04T23:59:32.776899Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:32.776899Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"bsV1lTX0sgQb9WTO4cCq7IiW4JZE/StNq3hUNrTPNWgkqxj9uPU/b5D5JH8CSBGJDG5q6RMylxawykcGTO+uAA==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:32.777395Z","signed_message":"canonical_sha256_bytes"},"source_id":"1905.11333","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:90e34415248df084001cafe6f0c8b5db1738e722fe07a2734af4b72e97a4bf4b","sha256:142be75cd4ac04708747a62ecd8d161c852b6ce676b9bc49279eb71aa61c0373"],"state_sha256":"6e85bf766be842362c608e8c64c23579a629b28144b80751026782bf306a7006"}