{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:M7CVU3U7SEYYZURQYONOUWRWIO","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":"64b819741cd19143bccce290856a650d87614963a7be6f8f0aff27c93cfc089e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-27T22:48:05Z","title_canon_sha256":"b4cca3318ec3083b91d11d82b80b26c7b72f75fff8f8996341833378e370ceb4"},"schema_version":"1.0","source":{"id":"2607.25129","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2607.25129","created_at":"2026-07-29T00:25:04Z"},{"alias_kind":"arxiv_version","alias_value":"2607.25129v1","created_at":"2026-07-29T00:25:04Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25129","created_at":"2026-07-29T00:25:04Z"},{"alias_kind":"pith_short_12","alias_value":"M7CVU3U7SEYY","created_at":"2026-07-29T00:25:04Z"},{"alias_kind":"pith_short_16","alias_value":"M7CVU3U7SEYYZURQ","created_at":"2026-07-29T00:25:04Z"},{"alias_kind":"pith_short_8","alias_value":"M7CVU3U7","created_at":"2026-07-29T00:25:04Z"}],"graph_snapshots":[{"event_id":"sha256:15b01d2f32288fcd285692ff213e9b203761c541df6584df8dbad0d05ed686f9","target":"graph","created_at":"2026-07-29T00:25:04Z","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/2607.25129/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Medical coding is the task of assigning a set of diagnosis and procedure codes for a hospitalization using recorded notes. It requires aggregating information from different parts of the text and focus to different sections for each individual code, making it a very difficult problem even for professional human coders. We model the task as a multi-label text classification problem. To overcome the mentioned difficulties, we propose a deep neural model consisting of a multi-layer temporal convolution network (TCN) followed by label-wise attention. While multi-layer TCN helps extract a global do","authors_text":"Alexander Fabbri, Dragomir Radev, Irene Li, Muhammed Yavuz Nuzumlal{\\i}","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-27T22:48:05Z","title":"Deep Label-Wise Attentive Temporal Convolutional Networks Improve Medical Coding"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25129","kind":"arxiv","version":1},"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":"sha256:709402be01eebbfd85de358d96c210f55e680b23ca254e99875a174a45a38383","target":"record","created_at":"2026-07-29T00:25:04Z","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":"64b819741cd19143bccce290856a650d87614963a7be6f8f0aff27c93cfc089e","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CL","submitted_at":"2026-07-27T22:48:05Z","title_canon_sha256":"b4cca3318ec3083b91d11d82b80b26c7b72f75fff8f8996341833378e370ceb4"},"schema_version":"1.0","source":{"id":"2607.25129","kind":"arxiv","version":1}},"canonical_sha256":"67c55a6e9f91318cd230c39aea5a3643ad83a13a2065c4437c6f04b9ddf974f2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"67c55a6e9f91318cd230c39aea5a3643ad83a13a2065c4437c6f04b9ddf974f2","first_computed_at":"2026-07-29T00:25:04.252513Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-29T00:25:04.252513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"4kriw4vTNrxJA1T+Bd5PRM9SJ1Lr/MWFplCjmPOBzwZc8uYz3yNV5COupCxEnSa8h0iB8lU0PzuoVjAxZVuTCQ==","signature_status":"signed_v1","signed_at":"2026-07-29T00:25:04.253388Z","signed_message":"canonical_sha256_bytes"},"source_id":"2607.25129","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:709402be01eebbfd85de358d96c210f55e680b23ca254e99875a174a45a38383","sha256:15b01d2f32288fcd285692ff213e9b203761c541df6584df8dbad0d05ed686f9"],"state_sha256":"76a3504be22f13abd81185b0ed0ad665bcf000e5f016776f776ce561c41393b2"}