{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:I3VWR7HZZLNHIVZJ3BPMB5JYWT","short_pith_number":"pith:I3VWR7HZ","canonical_record":{"source":{"id":"1810.10489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-24T16:51:35Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"775e85b39476690fd725f340f91feaf0898dd5f38a141787ef8fed6c94be0f1e","abstract_canon_sha256":"082e3d3c5515857b8ea32e72053283deda5705bc955ebf52ca29d4705b749bdc"},"schema_version":"1.0"},"canonical_sha256":"46eb68fcf9cada745729d85ec0f538b4ccff08aae2f0d884e53678e28eccb86e","source":{"kind":"arxiv","id":"1810.10489","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.10489","created_at":"2026-05-18T00:02:22Z"},{"alias_kind":"arxiv_version","alias_value":"1810.10489v1","created_at":"2026-05-18T00:02:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10489","created_at":"2026-05-18T00:02:22Z"},{"alias_kind":"pith_short_12","alias_value":"I3VWR7HZZLNH","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I3VWR7HZZLNHIVZJ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I3VWR7HZ","created_at":"2026-05-18T12:32:28Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:I3VWR7HZZLNHIVZJ3BPMB5JYWT","target":"record","payload":{"canonical_record":{"source":{"id":"1810.10489","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-24T16:51:35Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"775e85b39476690fd725f340f91feaf0898dd5f38a141787ef8fed6c94be0f1e","abstract_canon_sha256":"082e3d3c5515857b8ea32e72053283deda5705bc955ebf52ca29d4705b749bdc"},"schema_version":"1.0"},"canonical_sha256":"46eb68fcf9cada745729d85ec0f538b4ccff08aae2f0d884e53678e28eccb86e","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:02:22.926149Z","signature_b64":"25WuWd7O/y2bANbQGczo3zSvYZXSkUQLKyY+6IuYpsPbvTGTfq1heydDo7vAkIWahE0x0YlFJK3gp2gTZ+/7Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"46eb68fcf9cada745729d85ec0f538b4ccff08aae2f0d884e53678e28eccb86e","last_reissued_at":"2026-05-18T00:02:22.925515Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:02:22.925515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1810.10489","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":"2026-05-18T00:02:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"oCQllDzp18wWbCS1ZPDAyyzsuJULN/5HVB/Bqd201xPl12acIdr9QXbfuIZG7FzpFNqRPp1V6OdAXN6PCim1CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T09:16:49.775258Z"},"content_sha256":"3aa64167d9ffca7085d39343b0de8b39a161df1666badb865df2d191ae7c88f5","schema_version":"1.0","event_id":"sha256:3aa64167d9ffca7085d39343b0de8b39a161df1666badb865df2d191ae7c88f5"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:I3VWR7HZZLNHIVZJ3BPMB5JYWT","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Forecasting Individualized Disease Trajectories using Interpretable Deep Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Ahmed M. Alaa, Mihaela van der Schaar","submitted_at":"2018-10-24T16:51:35Z","abstract_excerpt":"Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either accurate predictions of patients prognoses or clinically interpretable representations of disease pathophysiology, but not both. In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability. Unlike Markovian state space models which assume memoryless dy"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10489","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":"2026-05-18T00:02:22Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"OEcnGbHzNE4BKbYxRD0Da5QAajeEUXLKTO8RMXywkXq0ZEfqG6gno/l++asaIjLf5Wr+2PM9hXIeZxC4OH4RAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-07T09:16:49.775711Z"},"content_sha256":"882d6fae1184d5dcd1e4875f7015ad7cd342fb4049b90136072b9698453202a5","schema_version":"1.0","event_id":"sha256:882d6fae1184d5dcd1e4875f7015ad7cd342fb4049b90136072b9698453202a5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT/bundle.json","state_url":"https://pith.science/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT/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":"2026-06-07T09:16:49Z","links":{"resolver":"https://pith.science/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT","bundle":"https://pith.science/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT/bundle.json","state":"https://pith.science/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT/state.json","well_known_bundle":"https://pith.science/.well-known/pith/I3VWR7HZZLNHIVZJ3BPMB5JYWT/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:I3VWR7HZZLNHIVZJ3BPMB5JYWT","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":"082e3d3c5515857b8ea32e72053283deda5705bc955ebf52ca29d4705b749bdc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-24T16:51:35Z","title_canon_sha256":"775e85b39476690fd725f340f91feaf0898dd5f38a141787ef8fed6c94be0f1e"},"schema_version":"1.0","source":{"id":"1810.10489","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1810.10489","created_at":"2026-05-18T00:02:22Z"},{"alias_kind":"arxiv_version","alias_value":"1810.10489v1","created_at":"2026-05-18T00:02:22Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1810.10489","created_at":"2026-05-18T00:02:22Z"},{"alias_kind":"pith_short_12","alias_value":"I3VWR7HZZLNH","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_16","alias_value":"I3VWR7HZZLNHIVZJ","created_at":"2026-05-18T12:32:28Z"},{"alias_kind":"pith_short_8","alias_value":"I3VWR7HZ","created_at":"2026-05-18T12:32:28Z"}],"graph_snapshots":[{"event_id":"sha256:882d6fae1184d5dcd1e4875f7015ad7cd342fb4049b90136072b9698453202a5","target":"graph","created_at":"2026-05-18T00:02:22Z","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":"Disease progression models are instrumental in predicting individual-level health trajectories and understanding disease dynamics. Existing models are capable of providing either accurate predictions of patients prognoses or clinically interpretable representations of disease pathophysiology, but not both. In this paper, we develop the phased attentive state space (PASS) model of disease progression, a deep probabilistic model that captures complex representations for disease progression while maintaining clinical interpretability. Unlike Markovian state space models which assume memoryless dy","authors_text":"Ahmed M. Alaa, Mihaela van der Schaar","cross_cats":["stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-24T16:51:35Z","title":"Forecasting Individualized Disease Trajectories using Interpretable Deep Learning"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1810.10489","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":"sha256:3aa64167d9ffca7085d39343b0de8b39a161df1666badb865df2d191ae7c88f5","target":"record","created_at":"2026-05-18T00:02:22Z","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":"082e3d3c5515857b8ea32e72053283deda5705bc955ebf52ca29d4705b749bdc","cross_cats_sorted":["stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2018-10-24T16:51:35Z","title_canon_sha256":"775e85b39476690fd725f340f91feaf0898dd5f38a141787ef8fed6c94be0f1e"},"schema_version":"1.0","source":{"id":"1810.10489","kind":"arxiv","version":1}},"canonical_sha256":"46eb68fcf9cada745729d85ec0f538b4ccff08aae2f0d884e53678e28eccb86e","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"46eb68fcf9cada745729d85ec0f538b4ccff08aae2f0d884e53678e28eccb86e","first_computed_at":"2026-05-18T00:02:22.925515Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:02:22.925515Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"25WuWd7O/y2bANbQGczo3zSvYZXSkUQLKyY+6IuYpsPbvTGTfq1heydDo7vAkIWahE0x0YlFJK3gp2gTZ+/7Aw==","signature_status":"signed_v1","signed_at":"2026-05-18T00:02:22.926149Z","signed_message":"canonical_sha256_bytes"},"source_id":"1810.10489","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:3aa64167d9ffca7085d39343b0de8b39a161df1666badb865df2d191ae7c88f5","sha256:882d6fae1184d5dcd1e4875f7015ad7cd342fb4049b90136072b9698453202a5"],"state_sha256":"4c3a3e43c0adbe3ba0522403d3cc16ac3c87903ba5b4b71110c4ba5579bf1795"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hEGnhFY70oI3QF63pUf0dR3Cw68UKvNxtd5nBhI37Zss/6aW6PPbGqZEzOO4NUk46SNwKyN4/hcDD0Cle1WWDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-07T09:16:49.778636Z","bundle_sha256":"8acc508d28062262ec2bdab680adbbcd2542e94ddd3b2adb70f2ef614f5cc08c"}}