{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:RHLZEVFADEYHQF6ALX2OCXQ5WG","short_pith_number":"pith:RHLZEVFA","schema_version":"1.0","canonical_sha256":"89d79254a019307817c05df4e15e1db1be7c7dee6324d804a4e1c332317a11fb","source":{"kind":"arxiv","id":"2412.18706","version":1},"attestation_state":"computed","paper":{"title":"SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu, Mohsen Nayebi Kerdabadi, Zijun Yao","submitted_at":"2024-12-24T23:35:42Z","abstract_excerpt":"Survival analysis (SA) models have been widely studied in mining electronic health records (EHRs), particularly in forecasting the risk of critical conditions for prioritizing high-risk patients. However, their vulnerability to adversarial attacks is much less explored in the literature. Developing black-box perturbation algorithms and evaluating their impact on state-of-the-art survival models brings two benefits to medical applications. First, it can effectively evaluate the robustness of models in pre-deployment testing. Also, exploring how subtle perturbations would result in significantly"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2412.18706","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-12-24T23:35:42Z","cross_cats_sorted":["cs.AI","cs.CR"],"title_canon_sha256":"25a8116fdfa14e14f4c4260dc0ad0dc570b41b16189828cfd5af438f229f41f2","abstract_canon_sha256":"14bf4f6ed5734c26b52e19c66c4b92f6e455cb6812a34cc267aa1ec14aec8db2"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:54:09.331140Z","signature_b64":"lbiivxjnJhjJ2nEnaf48t5/JP7P0uM+yO10vYwS1uaPcFZI/iovIQ9Lg8D2trSIxXh/INan27v94JPZqyIiGDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"89d79254a019307817c05df4e15e1db1be7c7dee6324d804a4e1c332317a11fb","last_reissued_at":"2026-07-05T09:54:09.330605Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:54:09.330605Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SurvAttack: Black-Box Attack On Survival Models through Ontology-Informed EHR Perturbation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.CR"],"primary_cat":"cs.LG","authors_text":"Arya Hadizadeh Moghaddam, Bin Liu, Mei Liu, Mohsen Nayebi Kerdabadi, Zijun Yao","submitted_at":"2024-12-24T23:35:42Z","abstract_excerpt":"Survival analysis (SA) models have been widely studied in mining electronic health records (EHRs), particularly in forecasting the risk of critical conditions for prioritizing high-risk patients. However, their vulnerability to adversarial attacks is much less explored in the literature. Developing black-box perturbation algorithms and evaluating their impact on state-of-the-art survival models brings two benefits to medical applications. First, it can effectively evaluate the robustness of models in pre-deployment testing. Also, exploring how subtle perturbations would result in significantly"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2412.18706","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/2412.18706/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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2412.18706","created_at":"2026-07-05T09:54:09.330674+00:00"},{"alias_kind":"arxiv_version","alias_value":"2412.18706v1","created_at":"2026-07-05T09:54:09.330674+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2412.18706","created_at":"2026-07-05T09:54:09.330674+00:00"},{"alias_kind":"pith_short_12","alias_value":"RHLZEVFADEYH","created_at":"2026-07-05T09:54:09.330674+00:00"},{"alias_kind":"pith_short_16","alias_value":"RHLZEVFADEYHQF6A","created_at":"2026-07-05T09:54:09.330674+00:00"},{"alias_kind":"pith_short_8","alias_value":"RHLZEVFA","created_at":"2026-07-05T09:54:09.330674+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG","json":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG.json","graph_json":"https://pith.science/api/pith-number/RHLZEVFADEYHQF6ALX2OCXQ5WG/graph.json","events_json":"https://pith.science/api/pith-number/RHLZEVFADEYHQF6ALX2OCXQ5WG/events.json","paper":"https://pith.science/paper/RHLZEVFA"},"agent_actions":{"view_html":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG","download_json":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG.json","view_paper":"https://pith.science/paper/RHLZEVFA","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2412.18706&json=true","fetch_graph":"https://pith.science/api/pith-number/RHLZEVFADEYHQF6ALX2OCXQ5WG/graph.json","fetch_events":"https://pith.science/api/pith-number/RHLZEVFADEYHQF6ALX2OCXQ5WG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG/action/storage_attestation","attest_author":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG/action/author_attestation","sign_citation":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG/action/citation_signature","submit_replication":"https://pith.science/pith/RHLZEVFADEYHQF6ALX2OCXQ5WG/action/replication_record"}},"created_at":"2026-07-05T09:54:09.330674+00:00","updated_at":"2026-07-05T09:54:09.330674+00:00"}