{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:25O3SF4PGMOO6HZFKTFA35UKKU","short_pith_number":"pith:25O3SF4P","canonical_record":{"source":{"id":"1903.08652","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T10:21:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4a730508a53d83fa10a2b541cc72c68b238b41aa90c62b1965018c2bce960c3d","abstract_canon_sha256":"31a91effd95fe4cf3562713eb7cc75466aa4f635ac151b05571838a01e2eb935"},"schema_version":"1.0"},"canonical_sha256":"d75db9178f331cef1f2554ca0df68a5508c448a9f7767b9beade846d7660c3f4","source":{"kind":"arxiv","id":"1903.08652","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.08652","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"arxiv_version","alias_value":"1903.08652v2","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08652","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"pith_short_12","alias_value":"25O3SF4PGMOO","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"pith_short_16","alias_value":"25O3SF4PGMOO6HZF","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"pith_short_8","alias_value":"25O3SF4P","created_at":"2026-07-04T23:59:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:25O3SF4PGMOO6HZFKTFA35UKKU","target":"record","payload":{"canonical_record":{"source":{"id":"1903.08652","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T10:21:42Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"4a730508a53d83fa10a2b541cc72c68b238b41aa90c62b1965018c2bce960c3d","abstract_canon_sha256":"31a91effd95fe4cf3562713eb7cc75466aa4f635ac151b05571838a01e2eb935"},"schema_version":"1.0"},"canonical_sha256":"d75db9178f331cef1f2554ca0df68a5508c448a9f7767b9beade846d7660c3f4","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:59:31.240294Z","signature_b64":"jNSzyl0XJUUJ/uLk2MB1mO1yTjyoXoqDN4a/O4ybP00vNNLtLhdwUAFgr4nMQB2cZKjnVzB9K2HqhrySuRHsDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d75db9178f331cef1f2554ca0df68a5508c448a9f7767b9beade846d7660c3f4","last_reissued_at":"2026-07-04T23:59:31.239854Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:59:31.239854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1903.08652","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:59:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qyHnVTlChiDkeDqGE9tzEYotezGhgVwYfbnWM6IdQexaez8WMCKO6ubbNI+3wGW468XuTiqEKXM45iO75S6HAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:29:59.012326Z"},"content_sha256":"67e0019dd1b3a2fb7b8e16a1d86d5493990749e277acf893c7a72707b0568749","schema_version":"1.0","event_id":"sha256:67e0019dd1b3a2fb7b8e16a1d86d5493990749e277acf893c7a72707b0568749"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:25O3SF4PGMOO6HZFKTFA35UKKU","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Haoran Li, Haoran Shi, Jian Tang, Luchen Liu, Ming Zhang, Zhiting Hu, Zichang Wang","submitted_at":"2019-03-20T10:21:42Z","abstract_excerpt":"Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a long time scale exhibit strongtemporal patterns, while events within a short time period tend to be disordered co-occurrence. We thus propose differentiated mechanisms to model clinical events at different time scales. Our model learns hierarchical representationsof event sequen"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08652","kind":"arxiv","version":2},"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/1903.08652/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-04T23:59:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"R7wDgVFlgJR35g5rgasHX+P75vYumJqYIrwLwhGk4Eb28USG2zhLwjxXiTLGi3HzsEbSFqMIqA4ptvYb4oZbAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-21T23:29:59.012915Z"},"content_sha256":"02af9cbe87fdcb7e408126d890f77ded438de4c1c1a48404acd5bb4ef3e3ac6b","schema_version":"1.0","event_id":"sha256:02af9cbe87fdcb7e408126d890f77ded438de4c1c1a48404acd5bb4ef3e3ac6b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/25O3SF4PGMOO6HZFKTFA35UKKU/bundle.json","state_url":"https://pith.science/pith/25O3SF4PGMOO6HZFKTFA35UKKU/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/25O3SF4PGMOO6HZFKTFA35UKKU/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-21T23:29:59Z","links":{"resolver":"https://pith.science/pith/25O3SF4PGMOO6HZFKTFA35UKKU","bundle":"https://pith.science/pith/25O3SF4PGMOO6HZFKTFA35UKKU/bundle.json","state":"https://pith.science/pith/25O3SF4PGMOO6HZFKTFA35UKKU/state.json","well_known_bundle":"https://pith.science/.well-known/pith/25O3SF4PGMOO6HZFKTFA35UKKU/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:25O3SF4PGMOO6HZFKTFA35UKKU","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":"31a91effd95fe4cf3562713eb7cc75466aa4f635ac151b05571838a01e2eb935","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T10:21:42Z","title_canon_sha256":"4a730508a53d83fa10a2b541cc72c68b238b41aa90c62b1965018c2bce960c3d"},"schema_version":"1.0","source":{"id":"1903.08652","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1903.08652","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"arxiv_version","alias_value":"1903.08652v2","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1903.08652","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"pith_short_12","alias_value":"25O3SF4PGMOO","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"pith_short_16","alias_value":"25O3SF4PGMOO6HZF","created_at":"2026-07-04T23:59:31Z"},{"alias_kind":"pith_short_8","alias_value":"25O3SF4P","created_at":"2026-07-04T23:59:31Z"}],"graph_snapshots":[{"event_id":"sha256:02af9cbe87fdcb7e408126d890f77ded438de4c1c1a48404acd5bb4ef3e3ac6b","target":"graph","created_at":"2026-07-04T23:59:31Z","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/1903.08652/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Clinical outcome prediction based on the Electronic Health Record (EHR) plays a crucial role in improving the quality of healthcare. Conventional deep sequential models fail to capture the rich temporal patterns encoded in the longand irregular clinical event sequences. We make the observation that clinical events at a long time scale exhibit strongtemporal patterns, while events within a short time period tend to be disordered co-occurrence. We thus propose differentiated mechanisms to model clinical events at different time scales. Our model learns hierarchical representationsof event sequen","authors_text":"Haoran Li, Haoran Shi, Jian Tang, Luchen Liu, Ming Zhang, Zhiting Hu, Zichang Wang","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T10:21:42Z","title":"Learning Hierarchical Representations of Electronic Health Records for Clinical Outcome Prediction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1903.08652","kind":"arxiv","version":2},"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":"sha256:67e0019dd1b3a2fb7b8e16a1d86d5493990749e277acf893c7a72707b0568749","target":"record","created_at":"2026-07-04T23:59:31Z","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":"31a91effd95fe4cf3562713eb7cc75466aa4f635ac151b05571838a01e2eb935","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-03-20T10:21:42Z","title_canon_sha256":"4a730508a53d83fa10a2b541cc72c68b238b41aa90c62b1965018c2bce960c3d"},"schema_version":"1.0","source":{"id":"1903.08652","kind":"arxiv","version":2}},"canonical_sha256":"d75db9178f331cef1f2554ca0df68a5508c448a9f7767b9beade846d7660c3f4","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d75db9178f331cef1f2554ca0df68a5508c448a9f7767b9beade846d7660c3f4","first_computed_at":"2026-07-04T23:59:31.239854Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:59:31.239854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"jNSzyl0XJUUJ/uLk2MB1mO1yTjyoXoqDN4a/O4ybP00vNNLtLhdwUAFgr4nMQB2cZKjnVzB9K2HqhrySuRHsDA==","signature_status":"signed_v1","signed_at":"2026-07-04T23:59:31.240294Z","signed_message":"canonical_sha256_bytes"},"source_id":"1903.08652","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:67e0019dd1b3a2fb7b8e16a1d86d5493990749e277acf893c7a72707b0568749","sha256:02af9cbe87fdcb7e408126d890f77ded438de4c1c1a48404acd5bb4ef3e3ac6b"],"state_sha256":"8348ba514b98931aa2d4ca1a0549ce96881f3af99d990ea28c449341c1467fd7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"M23DFqYMYLs/PgQcL6q1KeuI5Kv1UwQ7rghqCdFB3AJZGB3QSyTjgmCSDqG1j3mNkD3C4Np7B/FYNGa+WWWtDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-21T23:29:59.018295Z","bundle_sha256":"6a093356031fd295a26b77f8d9ad72c035f84d4798c3f21711f95ee94cb768fe"}}