{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:REHUJ7BVEK7C72EZXRAVGYYK5P","short_pith_number":"pith:REHUJ7BV","canonical_record":{"source":{"id":"1904.05815","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-04-11T16:13:22Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ec8fb7f69f94c8b78dccff291999573edf76a9f026dc401a7625a63bf2b37f6b","abstract_canon_sha256":"50eed7e367ad895b2f3ad9cd6c4b25f7bf20b9d4a55af71f69ffdb83574c76f5"},"schema_version":"1.0"},"canonical_sha256":"890f44fc3522be2fe899bc4153630aebe0e3cca10b95d10b8e12410d757b0627","source":{"kind":"arxiv","id":"1904.05815","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05815","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05815v1","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05815","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"REHUJ7BVEK7C","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"REHUJ7BVEK7C72EZ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"REHUJ7BV","created_at":"2026-05-18T12:33:27Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:REHUJ7BVEK7C72EZXRAVGYYK5P","target":"record","payload":{"canonical_record":{"source":{"id":"1904.05815","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-04-11T16:13:22Z","cross_cats_sorted":["cs.LG","stat.ML"],"title_canon_sha256":"ec8fb7f69f94c8b78dccff291999573edf76a9f026dc401a7625a63bf2b37f6b","abstract_canon_sha256":"50eed7e367ad895b2f3ad9cd6c4b25f7bf20b9d4a55af71f69ffdb83574c76f5"},"schema_version":"1.0"},"canonical_sha256":"890f44fc3522be2fe899bc4153630aebe0e3cca10b95d10b8e12410d757b0627","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:48:48.353051Z","signature_b64":"RPoo4yINPQ5Ymf+UU9CQqEvPuGUHz9eiAeO8OToQw9ujDBzUoHEyqGXZ9F0USYmmRivWmSCtFABrTDWWCmLJAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"890f44fc3522be2fe899bc4153630aebe0e3cca10b95d10b8e12410d757b0627","last_reissued_at":"2026-05-17T23:48:48.352548Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:48:48.352548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1904.05815","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":"2026-05-17T23:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Vy4Htmk/pJCpBh8tU+qM/ZKMQZEOu6lluidJGnQc/OyVk31prNXp7idE5bLNlHfWE7kgZCuXPWt1ytRRNM7LAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T23:43:59.761964Z"},"content_sha256":"fb59f24c00a0aab79e4d0735f216b2102b5c311f163196c46dc18a3845ae03b9","schema_version":"1.0","event_id":"sha256:fb59f24c00a0aab79e4d0735f216b2102b5c311f163196c46dc18a3845ae03b9"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:REHUJ7BVEK7C72EZXRAVGYYK5P","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.ML"],"primary_cat":"cs.NE","authors_text":"Jeroen Ruwaard, Mark Hoogendoorn, Ward van Breda","submitted_at":"2019-04-11T16:13:22Z","abstract_excerpt":"The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their future developments. However, these algorithms: (1) either focus on learning one generic model for all patients, providing general insights but often with limited predictive performance, or (2) learn individualized models from which it is hard to derive generic concepts. In this paper, we present a middle ground, namely parameterized dynamical"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05815","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":""},"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":"2026-05-17T23:48:48Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9e27fGDc16ca4fYJi/TbL4iuL4HaQUbh76Sek5/h0T/lSCkJd7vdh3AYIAIZBVf8JjXsDhxB3FOqonjVH+R7BQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-01T23:43:59.762329Z"},"content_sha256":"4ccd171acae11c53f88d390bf8ff6896721b719b29c145affd439f34a25e76e1","schema_version":"1.0","event_id":"sha256:4ccd171acae11c53f88d390bf8ff6896721b719b29c145affd439f34a25e76e1"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/REHUJ7BVEK7C72EZXRAVGYYK5P/bundle.json","state_url":"https://pith.science/pith/REHUJ7BVEK7C72EZXRAVGYYK5P/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/REHUJ7BVEK7C72EZXRAVGYYK5P/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":"2026-07-01T23:43:59Z","links":{"resolver":"https://pith.science/pith/REHUJ7BVEK7C72EZXRAVGYYK5P","bundle":"https://pith.science/pith/REHUJ7BVEK7C72EZXRAVGYYK5P/bundle.json","state":"https://pith.science/pith/REHUJ7BVEK7C72EZXRAVGYYK5P/state.json","well_known_bundle":"https://pith.science/.well-known/pith/REHUJ7BVEK7C72EZXRAVGYYK5P/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:REHUJ7BVEK7C72EZXRAVGYYK5P","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":"50eed7e367ad895b2f3ad9cd6c4b25f7bf20b9d4a55af71f69ffdb83574c76f5","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-04-11T16:13:22Z","title_canon_sha256":"ec8fb7f69f94c8b78dccff291999573edf76a9f026dc401a7625a63bf2b37f6b"},"schema_version":"1.0","source":{"id":"1904.05815","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1904.05815","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"arxiv_version","alias_value":"1904.05815v1","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1904.05815","created_at":"2026-05-17T23:48:48Z"},{"alias_kind":"pith_short_12","alias_value":"REHUJ7BVEK7C","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_16","alias_value":"REHUJ7BVEK7C72EZ","created_at":"2026-05-18T12:33:27Z"},{"alias_kind":"pith_short_8","alias_value":"REHUJ7BV","created_at":"2026-05-18T12:33:27Z"}],"graph_snapshots":[{"event_id":"sha256:4ccd171acae11c53f88d390bf8ff6896721b719b29c145affd439f34a25e76e1","target":"graph","created_at":"2026-05-17T23:48:48Z","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"},"paper":{"abstract_excerpt":"The huge wealth of data in the health domain can be exploited to create models that predict development of health states over time. Temporal learning algorithms are well suited to learn relationships between health states and make predictions about their future developments. However, these algorithms: (1) either focus on learning one generic model for all patients, providing general insights but often with limited predictive performance, or (2) learn individualized models from which it is hard to derive generic concepts. In this paper, we present a middle ground, namely parameterized dynamical","authors_text":"Jeroen Ruwaard, Mark Hoogendoorn, Ward van Breda","cross_cats":["cs.LG","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-04-11T16:13:22Z","title":"GP-HD: Using Genetic Programming to Generate Dynamical Systems Models for Health Care"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1904.05815","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:fb59f24c00a0aab79e4d0735f216b2102b5c311f163196c46dc18a3845ae03b9","target":"record","created_at":"2026-05-17T23:48:48Z","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":"50eed7e367ad895b2f3ad9cd6c4b25f7bf20b9d4a55af71f69ffdb83574c76f5","cross_cats_sorted":["cs.LG","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.NE","submitted_at":"2019-04-11T16:13:22Z","title_canon_sha256":"ec8fb7f69f94c8b78dccff291999573edf76a9f026dc401a7625a63bf2b37f6b"},"schema_version":"1.0","source":{"id":"1904.05815","kind":"arxiv","version":1}},"canonical_sha256":"890f44fc3522be2fe899bc4153630aebe0e3cca10b95d10b8e12410d757b0627","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"890f44fc3522be2fe899bc4153630aebe0e3cca10b95d10b8e12410d757b0627","first_computed_at":"2026-05-17T23:48:48.352548Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:48:48.352548Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"RPoo4yINPQ5Ymf+UU9CQqEvPuGUHz9eiAeO8OToQw9ujDBzUoHEyqGXZ9F0USYmmRivWmSCtFABrTDWWCmLJAw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:48:48.353051Z","signed_message":"canonical_sha256_bytes"},"source_id":"1904.05815","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:fb59f24c00a0aab79e4d0735f216b2102b5c311f163196c46dc18a3845ae03b9","sha256:4ccd171acae11c53f88d390bf8ff6896721b719b29c145affd439f34a25e76e1"],"state_sha256":"7f9d8931fcea2e1f9a65a93fb960d95195c8cba03df18323e79e26781c2dba33"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gQH+rIhTVSs7KmC0JCERZXasgkmOYtm3BkEqgaHagqFRGaUE1ofYLuEA9y+pP04WNuqml92m7OcZ5zJKsq9RAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-01T23:43:59.764506Z","bundle_sha256":"0ef8199bd0941ce55403588c76f5d180800e3d97c1f49a00fa59456de9e2c8e8"}}