{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:7KDSIDQCFLAGWW5MCWNOIHTMT7","short_pith_number":"pith:7KDSIDQC","canonical_record":{"source":{"id":"2305.14961","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T09:56:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7ebf3a6072a9c0c195712242e76e375cf557a27b6fffca8d2df7b865b6e738d9","abstract_canon_sha256":"9f9d481ce6d965102626e8cb49e16d0c6d0089b720cec6f3595d01436901c030"},"schema_version":"1.0"},"canonical_sha256":"fa87240e022ac06b5bac159ae41e6c9fc0ea34824e5552d5adb625afe4bbfd06","source":{"kind":"arxiv","id":"2305.14961","version":4},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14961","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14961v4","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14961","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"pith_short_12","alias_value":"7KDSIDQCFLAG","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"pith_short_16","alias_value":"7KDSIDQCFLAGWW5M","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"pith_short_8","alias_value":"7KDSIDQC","created_at":"2026-07-05T07:47:56Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:7KDSIDQCFLAGWW5MCWNOIHTMT7","target":"record","payload":{"canonical_record":{"source":{"id":"2305.14961","kind":"arxiv","version":4},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T09:56:20Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"7ebf3a6072a9c0c195712242e76e375cf557a27b6fffca8d2df7b865b6e738d9","abstract_canon_sha256":"9f9d481ce6d965102626e8cb49e16d0c6d0089b720cec6f3595d01436901c030"},"schema_version":"1.0"},"canonical_sha256":"fa87240e022ac06b5bac159ae41e6c9fc0ea34824e5552d5adb625afe4bbfd06","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:47:56.869874Z","signature_b64":"Pksr14Inq1CBWpXLEfQMMZk8JVCmzlh/uNlu3Hg31R5KUs07gXnkc5mKofA3CbrUUgY9JynU8exaIMwvZ+a6Dw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fa87240e022ac06b5bac159ae41e6c9fc0ea34824e5552d5adb625afe4bbfd06","last_reissued_at":"2026-07-05T07:47:56.869383Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:47:56.869383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2305.14961","source_version":4,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:47:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"yhYvdAOSXF20O9erRAnGv0Awcyc5u6EYVlH53dK+lyHOZerdpYdDePZFYK1uOJZJp5M+mK5JVELrq2wTR+ysBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:40:31.223778Z"},"content_sha256":"83b98b5304cf67b44dedaa24a6ac9e2004008b35cf62179af748916dba25f955","schema_version":"1.0","event_id":"sha256:83b98b5304cf67b44dedaa24a6ac9e2004008b35cf62179af748916dba25f955"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:7KDSIDQCFLAGWW5MCWNOIHTMT7","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Learning for Survival Analysis: A Review","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Andreas Bender, Bernd Bischl, Philipp Kopper, Raphael Sonabend, Simon Wiegrebe","submitted_at":"2023-05-24T09:56:20Z","abstract_excerpt":"The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In this work, we conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In summary, the reviewed methods often address only a small subset of tasks relevant to time-to-event data - e.g., single-risk right-censored data - and neglect to incorpor"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14961","kind":"arxiv","version":4},"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/2305.14961/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":"2026-07-05T07:47:56Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hXrMPwf8fN2Qoe/+VvQh6nRA3ehNkYlB9eqslBBRmpt1bbUIt6o4xEkKOGR6n5Wy0qhtlRZeLnyYge11jLRBBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-10T00:40:31.224289Z"},"content_sha256":"68b443358ad43f17d3c92aa175ff675e2ff2eb3428b00e7b1ec0ec3e822d28dd","schema_version":"1.0","event_id":"sha256:68b443358ad43f17d3c92aa175ff675e2ff2eb3428b00e7b1ec0ec3e822d28dd"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7/bundle.json","state_url":"https://pith.science/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7/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":"2026-08-10T00:40:31Z","links":{"resolver":"https://pith.science/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7","bundle":"https://pith.science/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7/bundle.json","state":"https://pith.science/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7/state.json","well_known_bundle":"https://pith.science/.well-known/pith/7KDSIDQCFLAGWW5MCWNOIHTMT7/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:7KDSIDQCFLAGWW5MCWNOIHTMT7","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":"9f9d481ce6d965102626e8cb49e16d0c6d0089b720cec6f3595d01436901c030","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T09:56:20Z","title_canon_sha256":"7ebf3a6072a9c0c195712242e76e375cf557a27b6fffca8d2df7b865b6e738d9"},"schema_version":"1.0","source":{"id":"2305.14961","kind":"arxiv","version":4}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2305.14961","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"arxiv_version","alias_value":"2305.14961v4","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2305.14961","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"pith_short_12","alias_value":"7KDSIDQCFLAG","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"pith_short_16","alias_value":"7KDSIDQCFLAGWW5M","created_at":"2026-07-05T07:47:56Z"},{"alias_kind":"pith_short_8","alias_value":"7KDSIDQC","created_at":"2026-07-05T07:47:56Z"}],"graph_snapshots":[{"event_id":"sha256:68b443358ad43f17d3c92aa175ff675e2ff2eb3428b00e7b1ec0ec3e822d28dd","target":"graph","created_at":"2026-07-05T07:47:56Z","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/2305.14961/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"The influx of deep learning (DL) techniques into the field of survival analysis in recent years has led to substantial methodological progress; for instance, learning from unstructured or high-dimensional data such as images, text or omics data. In this work, we conduct a comprehensive systematic review of DL-based methods for time-to-event analysis, characterizing them according to both survival- and DL-related attributes. In summary, the reviewed methods often address only a small subset of tasks relevant to time-to-event data - e.g., single-risk right-censored data - and neglect to incorpor","authors_text":"Andreas Bender, Bernd Bischl, Philipp Kopper, Raphael Sonabend, Simon Wiegrebe","cross_cats":["cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T09:56:20Z","title":"Deep Learning for Survival Analysis: A Review"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2305.14961","kind":"arxiv","version":4},"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":"sha256:83b98b5304cf67b44dedaa24a6ac9e2004008b35cf62179af748916dba25f955","target":"record","created_at":"2026-07-05T07:47:56Z","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":"9f9d481ce6d965102626e8cb49e16d0c6d0089b720cec6f3595d01436901c030","cross_cats_sorted":["cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"stat.ML","submitted_at":"2023-05-24T09:56:20Z","title_canon_sha256":"7ebf3a6072a9c0c195712242e76e375cf557a27b6fffca8d2df7b865b6e738d9"},"schema_version":"1.0","source":{"id":"2305.14961","kind":"arxiv","version":4}},"canonical_sha256":"fa87240e022ac06b5bac159ae41e6c9fc0ea34824e5552d5adb625afe4bbfd06","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"fa87240e022ac06b5bac159ae41e6c9fc0ea34824e5552d5adb625afe4bbfd06","first_computed_at":"2026-07-05T07:47:56.869383Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:47:56.869383Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Pksr14Inq1CBWpXLEfQMMZk8JVCmzlh/uNlu3Hg31R5KUs07gXnkc5mKofA3CbrUUgY9JynU8exaIMwvZ+a6Dw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:47:56.869874Z","signed_message":"canonical_sha256_bytes"},"source_id":"2305.14961","source_kind":"arxiv","source_version":4}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:83b98b5304cf67b44dedaa24a6ac9e2004008b35cf62179af748916dba25f955","sha256:68b443358ad43f17d3c92aa175ff675e2ff2eb3428b00e7b1ec0ec3e822d28dd"],"state_sha256":"fd993b5005481724a436a0b567c1e676123dda4df3aaa15e87cd929215b1f478"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"y+FRA8RNIw/5h3V9sI75xInBl5jVcxbpwyfLgZc2CdWp/Q3509sbR56p5A2cL9mDArUBxBY+pKTjtHnhsg+HDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-10T00:40:31.228375Z","bundle_sha256":"339be80ebef36f44a93491df1770300058480e9ff3969856a5b34820a8e246f9"}}