{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:VIP3BS5FRL44NW5KLD3KKZR7HE","short_pith_number":"pith:VIP3BS5F","canonical_record":{"source":{"id":"1910.02951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-07T14:04:17Z","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"title_canon_sha256":"519464028b6ba426bc753e75e28e5c7414edf2db4073f59dcaa317d7cbd7bce1","abstract_canon_sha256":"40ff498645e5b7beb2f2dfcf2ceeef812dc876c3eadf0cf49581a9a3cddff351"},"schema_version":"1.0"},"canonical_sha256":"aa1fb0cba58af9c6dbaa58f6a5663f3909715ae7fe6c1d3a5a55d1ec3f62b66a","source":{"kind":"arxiv","id":"1910.02951","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02951","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02951v1","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02951","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"pith_short_12","alias_value":"VIP3BS5FRL44","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"pith_short_16","alias_value":"VIP3BS5FRL44NW5K","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"pith_short_8","alias_value":"VIP3BS5F","created_at":"2026-07-05T00:10:16Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:VIP3BS5FRL44NW5KLD3KKZR7HE","target":"record","payload":{"canonical_record":{"source":{"id":"1910.02951","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-07T14:04:17Z","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"title_canon_sha256":"519464028b6ba426bc753e75e28e5c7414edf2db4073f59dcaa317d7cbd7bce1","abstract_canon_sha256":"40ff498645e5b7beb2f2dfcf2ceeef812dc876c3eadf0cf49581a9a3cddff351"},"schema_version":"1.0"},"canonical_sha256":"aa1fb0cba58af9c6dbaa58f6a5663f3909715ae7fe6c1d3a5a55d1ec3f62b66a","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:10:16.505164Z","signature_b64":"eX2TY8tQJuMJmWbYQbu6BZzFPS+Uet+hgHneF/qlEuFGd6ByLaxCVpCr4YWOhLXyAR8vUsZ31UYeMCE2N6RuCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"aa1fb0cba58af9c6dbaa58f6a5663f3909715ae7fe6c1d3a5a55d1ec3f62b66a","last_reissued_at":"2026-07-05T00:10:16.504794Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:10:16.504794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1910.02951","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T00:10:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"b4sWQJSxXnPNGoU05hvVjmKxM1fj9PKQDWNf1m6Q7iHZSjogSL6F17JTQcI4FK6qoyBrAEe0BB7s7tZSUDHmBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T10:49:56.006269Z"},"content_sha256":"eaf3a147c96bc0f0b99326cfba396ac4a5489a8b8cd7c24ad9f610ffbc4e6994","schema_version":"1.0","event_id":"sha256:eaf3a147c96bc0f0b99326cfba396ac4a5489a8b8cd7c24ad9f610ffbc4e6994"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:VIP3BS5FRL44NW5KLD3KKZR7HE","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","eess.IV","stat.ML"],"primary_cat":"q-bio.QM","authors_text":"Antonio de Marvao, Axel Gandy, Daniel Rueckert, Declan P O'Regan, Nicol\\`o Savioli, Shihao Jin, Timothy JW Dawes","submitted_at":"2019-10-07T14:04:17Z","abstract_excerpt":"In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure. Different methods are evaluated to find the optimal way to insert conventional covariates into deep prediction networks. Correlation analysis between autoencoder latent codes and covariate features is used to examine how these predictors interact. We believe that similar approaches could also be used to introduce knowledge of genetic variants to such "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02951","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/1910.02951/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":"2026-07-05T00:10:16Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"B3pfdHZ1XuVaujLfzBMTYlXl7VCqPaCOBNhHuUOV65NS2mcWi17/rUf+XwnKBMdzRZzo6x3AevBLrxn1h4MECw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-18T10:49:56.006973Z"},"content_sha256":"d4ad3f3e6b021ad030e57f95668953bd412965f371fb3d2d0591d1cb3015a6d9","schema_version":"1.0","event_id":"sha256:d4ad3f3e6b021ad030e57f95668953bd412965f371fb3d2d0591d1cb3015a6d9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VIP3BS5FRL44NW5KLD3KKZR7HE/bundle.json","state_url":"https://pith.science/pith/VIP3BS5FRL44NW5KLD3KKZR7HE/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VIP3BS5FRL44NW5KLD3KKZR7HE/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":"2026-08-18T10:49:56Z","links":{"resolver":"https://pith.science/pith/VIP3BS5FRL44NW5KLD3KKZR7HE","bundle":"https://pith.science/pith/VIP3BS5FRL44NW5KLD3KKZR7HE/bundle.json","state":"https://pith.science/pith/VIP3BS5FRL44NW5KLD3KKZR7HE/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VIP3BS5FRL44NW5KLD3KKZR7HE/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:VIP3BS5FRL44NW5KLD3KKZR7HE","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":"40ff498645e5b7beb2f2dfcf2ceeef812dc876c3eadf0cf49581a9a3cddff351","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-07T14:04:17Z","title_canon_sha256":"519464028b6ba426bc753e75e28e5c7414edf2db4073f59dcaa317d7cbd7bce1"},"schema_version":"1.0","source":{"id":"1910.02951","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02951","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02951v1","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02951","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"pith_short_12","alias_value":"VIP3BS5FRL44","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"pith_short_16","alias_value":"VIP3BS5FRL44NW5K","created_at":"2026-07-05T00:10:16Z"},{"alias_kind":"pith_short_8","alias_value":"VIP3BS5F","created_at":"2026-07-05T00:10:16Z"}],"graph_snapshots":[{"event_id":"sha256:d4ad3f3e6b021ad030e57f95668953bd412965f371fb3d2d0591d1cb3015a6d9","target":"graph","created_at":"2026-07-05T00:10:16Z","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/1910.02951/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"In this work, a novel approach is proposed for joint analysis of high dimensional time-resolved cardiac motion features obtained from segmented cardiac MRI and low dimensional clinical risk factors to improve survival prediction in heart failure. Different methods are evaluated to find the optimal way to insert conventional covariates into deep prediction networks. Correlation analysis between autoencoder latent codes and covariate features is used to examine how these predictors interact. We believe that similar approaches could also be used to introduce knowledge of genetic variants to such ","authors_text":"Antonio de Marvao, Axel Gandy, Daniel Rueckert, Declan P O'Regan, Nicol\\`o Savioli, Shihao Jin, Timothy JW Dawes","cross_cats":["cs.LG","eess.IV","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-07T14:04:17Z","title":"Joint analysis of clinical risk factors and 4D cardiac motion for survival prediction using a hybrid deep learning network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02951","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:eaf3a147c96bc0f0b99326cfba396ac4a5489a8b8cd7c24ad9f610ffbc4e6994","target":"record","created_at":"2026-07-05T00:10:16Z","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":"40ff498645e5b7beb2f2dfcf2ceeef812dc876c3eadf0cf49581a9a3cddff351","cross_cats_sorted":["cs.LG","eess.IV","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"q-bio.QM","submitted_at":"2019-10-07T14:04:17Z","title_canon_sha256":"519464028b6ba426bc753e75e28e5c7414edf2db4073f59dcaa317d7cbd7bce1"},"schema_version":"1.0","source":{"id":"1910.02951","kind":"arxiv","version":1}},"canonical_sha256":"aa1fb0cba58af9c6dbaa58f6a5663f3909715ae7fe6c1d3a5a55d1ec3f62b66a","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"aa1fb0cba58af9c6dbaa58f6a5663f3909715ae7fe6c1d3a5a55d1ec3f62b66a","first_computed_at":"2026-07-05T00:10:16.504794Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:10:16.504794Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"eX2TY8tQJuMJmWbYQbu6BZzFPS+Uet+hgHneF/qlEuFGd6ByLaxCVpCr4YWOhLXyAR8vUsZ31UYeMCE2N6RuCA==","signature_status":"signed_v1","signed_at":"2026-07-05T00:10:16.505164Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.02951","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:eaf3a147c96bc0f0b99326cfba396ac4a5489a8b8cd7c24ad9f610ffbc4e6994","sha256:d4ad3f3e6b021ad030e57f95668953bd412965f371fb3d2d0591d1cb3015a6d9"],"state_sha256":"b85305d57da6d42635e5adced24a82ba705169b22e1f591d9cbf84e6f88ed687"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ax7ZmIbBKBvKBHgb3s3Jtb8yjnKLk5+PIyzMwvZjnw3YbJHqCpdVU/3qX2nwrQMiliXAzzH7DiZyS295xV1GAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-18T10:49:56.013347Z","bundle_sha256":"93b93249b45ebcc4e23c6f4dc67db35d8ffdf14aa549dde98e72294c13fe02c8"}}