{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:ZDM76GNW2NCBEWKZU3MOHOVL2T","short_pith_number":"pith:ZDM76GNW","canonical_record":{"source":{"id":"2502.02109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T08:43:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"01ed9107ab24963ee3a7b782b0e8d0521a02215a07bf9359d624b6c00821c033","abstract_canon_sha256":"e0de4371b8566ce1558d49134c53cb716ba649bfdde93f2c813b33f3dfebd261"},"schema_version":"1.0"},"canonical_sha256":"c8d9ff19b6d344125959a6d8e3baabd4e201683b89bc476c945339c603b5c768","source":{"kind":"arxiv","id":"2502.02109","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.02109","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"arxiv_version","alias_value":"2502.02109v1","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02109","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"pith_short_12","alias_value":"ZDM76GNW2NCB","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"pith_short_16","alias_value":"ZDM76GNW2NCBEWKZ","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"pith_short_8","alias_value":"ZDM76GNW","created_at":"2026-07-05T10:09:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:ZDM76GNW2NCBEWKZU3MOHOVL2T","target":"record","payload":{"canonical_record":{"source":{"id":"2502.02109","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T08:43:39Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"01ed9107ab24963ee3a7b782b0e8d0521a02215a07bf9359d624b6c00821c033","abstract_canon_sha256":"e0de4371b8566ce1558d49134c53cb716ba649bfdde93f2c813b33f3dfebd261"},"schema_version":"1.0"},"canonical_sha256":"c8d9ff19b6d344125959a6d8e3baabd4e201683b89bc476c945339c603b5c768","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:09:23.841614Z","signature_b64":"uHoTalf104VvYPtmjfwC6NA/GRft64A8GbSTyIQ6SpygLbFxyNRK/uLgUYZAQyeBgQOFh9ReATJtghmsX/E9Dg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c8d9ff19b6d344125959a6d8e3baabd4e201683b89bc476c945339c603b5c768","last_reissued_at":"2026-07-05T10:09:23.841144Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:09:23.841144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2502.02109","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":"2026-07-05T10:09:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"TAbiXAfXYz1TQsaucMPuqp5oMsOI5FErIzmEO8iFc1xhozkCbZf6G+tXrRPB3o/vkryfRBTBNcRxNbMmm4QlCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T00:34:18.231509Z"},"content_sha256":"7f7a14cb5d95ba467f766322bbf501abda8ee252fd548d2769bf7c59874e81b3","schema_version":"1.0","event_id":"sha256:7f7a14cb5d95ba467f766322bbf501abda8ee252fd548d2769bf7c59874e81b3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:ZDM76GNW2NCBEWKZU3MOHOVL2T","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Jinli Suo, Kunlun He, Qin Zhong, Xinxin Song, Yuxiao Cheng, Ziqian Wang","submitted_at":"2025-02-04T08:43:39Z","abstract_excerpt":"Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney injury, acute myocardial infarction, or circulatory failure. DL models have proven to be powerful tools for various tasks but come with the cost of lacking interpretability and limited generalizability, hindering their clinical applications. To develop a practical EWS system applicable to various outcomes, we propose causally-informed explainable early prediction model, which leverages causal discovery to identify the "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02109","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/2502.02109/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":"2026-07-05T10:09:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X6jFRk/JqEIJSKg7L8pxk+pydHuT+YTzMhbHkSSRQQeH275BBxzOuaQXvQg811jUK8JjMY0MckCgPsyFnP8UCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-23T00:34:18.231984Z"},"content_sha256":"3e2e058357f16a85965a14303d32eea2c5da643e954551a446a3a2a5645f878a","schema_version":"1.0","event_id":"sha256:3e2e058357f16a85965a14303d32eea2c5da643e954551a446a3a2a5645f878a"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T/bundle.json","state_url":"https://pith.science/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T/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":"2026-08-23T00:34:18Z","links":{"resolver":"https://pith.science/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T","bundle":"https://pith.science/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T/bundle.json","state":"https://pith.science/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ZDM76GNW2NCBEWKZU3MOHOVL2T/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:ZDM76GNW2NCBEWKZU3MOHOVL2T","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":"e0de4371b8566ce1558d49134c53cb716ba649bfdde93f2c813b33f3dfebd261","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T08:43:39Z","title_canon_sha256":"01ed9107ab24963ee3a7b782b0e8d0521a02215a07bf9359d624b6c00821c033"},"schema_version":"1.0","source":{"id":"2502.02109","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2502.02109","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"arxiv_version","alias_value":"2502.02109v1","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2502.02109","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"pith_short_12","alias_value":"ZDM76GNW2NCB","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"pith_short_16","alias_value":"ZDM76GNW2NCBEWKZ","created_at":"2026-07-05T10:09:23Z"},{"alias_kind":"pith_short_8","alias_value":"ZDM76GNW","created_at":"2026-07-05T10:09:23Z"}],"graph_snapshots":[{"event_id":"sha256:3e2e058357f16a85965a14303d32eea2c5da643e954551a446a3a2a5645f878a","target":"graph","created_at":"2026-07-05T10:09:23Z","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/2502.02109/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Recent advances in deep learning (DL) have prompted the development of high-performing early warning score (EWS) systems, predicting clinical deteriorations such as acute kidney injury, acute myocardial infarction, or circulatory failure. DL models have proven to be powerful tools for various tasks but come with the cost of lacking interpretability and limited generalizability, hindering their clinical applications. To develop a practical EWS system applicable to various outcomes, we propose causally-informed explainable early prediction model, which leverages causal discovery to identify the ","authors_text":"Jinli Suo, Kunlun He, Qin Zhong, Xinxin Song, Yuxiao Cheng, Ziqian Wang","cross_cats":["cs.AI"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T08:43:39Z","title":"Causally-informed Deep Learning towards Explainable and Generalizable Outcomes Prediction in Critical Care"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2502.02109","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":"sha256:7f7a14cb5d95ba467f766322bbf501abda8ee252fd548d2769bf7c59874e81b3","target":"record","created_at":"2026-07-05T10:09:23Z","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":"e0de4371b8566ce1558d49134c53cb716ba649bfdde93f2c813b33f3dfebd261","cross_cats_sorted":["cs.AI"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-02-04T08:43:39Z","title_canon_sha256":"01ed9107ab24963ee3a7b782b0e8d0521a02215a07bf9359d624b6c00821c033"},"schema_version":"1.0","source":{"id":"2502.02109","kind":"arxiv","version":1}},"canonical_sha256":"c8d9ff19b6d344125959a6d8e3baabd4e201683b89bc476c945339c603b5c768","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"c8d9ff19b6d344125959a6d8e3baabd4e201683b89bc476c945339c603b5c768","first_computed_at":"2026-07-05T10:09:23.841144Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:09:23.841144Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"uHoTalf104VvYPtmjfwC6NA/GRft64A8GbSTyIQ6SpygLbFxyNRK/uLgUYZAQyeBgQOFh9ReATJtghmsX/E9Dg==","signature_status":"signed_v1","signed_at":"2026-07-05T10:09:23.841614Z","signed_message":"canonical_sha256_bytes"},"source_id":"2502.02109","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f7a14cb5d95ba467f766322bbf501abda8ee252fd548d2769bf7c59874e81b3","sha256:3e2e058357f16a85965a14303d32eea2c5da643e954551a446a3a2a5645f878a"],"state_sha256":"2e603b0c181f184289db4cc824ffbf1258f7ecebaf67baef24994f8018bddbd0"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8gpEsPeSSor9SVuriqyH5oHBE2gFyy0oEa2ZP7yYzckNAl05jCGdE5rlSgjmBjCGYEdQlUr0CrfX+4F2JmtNCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-23T00:34:18.236343Z","bundle_sha256":"c151eca94e322ca1813ec862f966289ad172f53946947dbe9d99eb2fba457cf9"}}