{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:6A2AQYSPPIPON5DNNDORMICRMS","short_pith_number":"pith:6A2AQYSP","canonical_record":{"source":{"id":"2108.01701","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-03T18:50:26Z","cross_cats_sorted":[],"title_canon_sha256":"c60bc33522721600b58502f90b3f675d723f01dbde488bbf8a6c0cfedbe4929f","abstract_canon_sha256":"aeee6e7cf310cb11c6eb0c1041f89f281554b44f116cdb85a8fc8e9f0864ac80"},"schema_version":"1.0"},"canonical_sha256":"f03408624f7a1ee6f46d68dd1620516488ac0e2a56da0e12bb347954c462ccd1","source":{"kind":"arxiv","id":"2108.01701","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.01701","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"arxiv_version","alias_value":"2108.01701v2","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.01701","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"pith_short_12","alias_value":"6A2AQYSPPIPO","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"pith_short_16","alias_value":"6A2AQYSPPIPON5DN","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"pith_short_8","alias_value":"6A2AQYSP","created_at":"2026-07-05T03:03:30Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:6A2AQYSPPIPON5DNNDORMICRMS","target":"record","payload":{"canonical_record":{"source":{"id":"2108.01701","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-03T18:50:26Z","cross_cats_sorted":[],"title_canon_sha256":"c60bc33522721600b58502f90b3f675d723f01dbde488bbf8a6c0cfedbe4929f","abstract_canon_sha256":"aeee6e7cf310cb11c6eb0c1041f89f281554b44f116cdb85a8fc8e9f0864ac80"},"schema_version":"1.0"},"canonical_sha256":"f03408624f7a1ee6f46d68dd1620516488ac0e2a56da0e12bb347954c462ccd1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:03:30.003623Z","signature_b64":"OrEHEGUKH7qXALfTpBImNvZ1AIYdjpOhVjm8LcPU9xeKhXsckQji88iM3JVe7s65PAzDoWJnLEFHoYCCiS1vAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f03408624f7a1ee6f46d68dd1620516488ac0e2a56da0e12bb347954c462ccd1","last_reissued_at":"2026-07-05T03:03:30.003231Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:03:30.003231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2108.01701","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T03:03:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"+UuJV9XxVjeMzql6B+vEhGJzV9MFWHRhWqe2qsPZy7DKG3PLMNipnxXyfzF3VExrZ95mTYVDVbOcuw3fKINsBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:21:47.283866Z"},"content_sha256":"472226eb8a29e80e5718622585a55d8b4c80fe973750f70f96bf8a1823ea8653","schema_version":"1.0","event_id":"sha256:472226eb8a29e80e5718622585a55d8b4c80fe973750f70f96bf8a1823ea8653"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:6A2AQYSPPIPON5DNNDORMICRMS","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Categorical EHR Imputation with Generative Adversarial Nets","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Peter A. Fasching, Volker Tresp, Yinchong Yang, Zhiliang Wu","submitted_at":"2021-08-03T18:50:26Z","abstract_excerpt":"Electronic Health Records often suffer from missing data, which poses a major problem in clinical practice and clinical studies. A novel approach for dealing with missing data are Generative Adversarial Nets (GANs), which have been generating huge research interest in image generation and transformation. Recently, researchers have attempted to apply GANs to missing data generation and imputation for EHR data: a major challenge here is the categorical nature of the data. State-of-the-art solutions to the GAN-based generation of categorical data involve either reinforcement learning, or learning"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.01701","kind":"arxiv","version":2},"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/2108.01701/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":"2026-07-05T03:03:30Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Qkg4bqElRAx8TlgX6ySQre1Bx9yuOPqTbFTR38a1bEQSqS+5PjmXdSzfmSTFGopuo1Dqu5oHT5k7Ku/VtCeGCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-04T19:21:47.284367Z"},"content_sha256":"64ae23a7d9aa0371b4de31b1c65dc963f68e69820d55b008e76167d032e653f3","schema_version":"1.0","event_id":"sha256:64ae23a7d9aa0371b4de31b1c65dc963f68e69820d55b008e76167d032e653f3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/6A2AQYSPPIPON5DNNDORMICRMS/bundle.json","state_url":"https://pith.science/pith/6A2AQYSPPIPON5DNNDORMICRMS/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/6A2AQYSPPIPON5DNNDORMICRMS/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":"2026-08-04T19:21:47Z","links":{"resolver":"https://pith.science/pith/6A2AQYSPPIPON5DNNDORMICRMS","bundle":"https://pith.science/pith/6A2AQYSPPIPON5DNNDORMICRMS/bundle.json","state":"https://pith.science/pith/6A2AQYSPPIPON5DNNDORMICRMS/state.json","well_known_bundle":"https://pith.science/.well-known/pith/6A2AQYSPPIPON5DNNDORMICRMS/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:6A2AQYSPPIPON5DNNDORMICRMS","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":"aeee6e7cf310cb11c6eb0c1041f89f281554b44f116cdb85a8fc8e9f0864ac80","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-03T18:50:26Z","title_canon_sha256":"c60bc33522721600b58502f90b3f675d723f01dbde488bbf8a6c0cfedbe4929f"},"schema_version":"1.0","source":{"id":"2108.01701","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2108.01701","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"arxiv_version","alias_value":"2108.01701v2","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2108.01701","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"pith_short_12","alias_value":"6A2AQYSPPIPO","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"pith_short_16","alias_value":"6A2AQYSPPIPON5DN","created_at":"2026-07-05T03:03:30Z"},{"alias_kind":"pith_short_8","alias_value":"6A2AQYSP","created_at":"2026-07-05T03:03:30Z"}],"graph_snapshots":[{"event_id":"sha256:64ae23a7d9aa0371b4de31b1c65dc963f68e69820d55b008e76167d032e653f3","target":"graph","created_at":"2026-07-05T03:03:30Z","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/2108.01701/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Electronic Health Records often suffer from missing data, which poses a major problem in clinical practice and clinical studies. A novel approach for dealing with missing data are Generative Adversarial Nets (GANs), which have been generating huge research interest in image generation and transformation. Recently, researchers have attempted to apply GANs to missing data generation and imputation for EHR data: a major challenge here is the categorical nature of the data. State-of-the-art solutions to the GAN-based generation of categorical data involve either reinforcement learning, or learning","authors_text":"Peter A. Fasching, Volker Tresp, Yinchong Yang, Zhiliang Wu","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-03T18:50:26Z","title":"Categorical EHR Imputation with Generative Adversarial Nets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2108.01701","kind":"arxiv","version":2},"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":"sha256:472226eb8a29e80e5718622585a55d8b4c80fe973750f70f96bf8a1823ea8653","target":"record","created_at":"2026-07-05T03:03:30Z","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":"aeee6e7cf310cb11c6eb0c1041f89f281554b44f116cdb85a8fc8e9f0864ac80","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-08-03T18:50:26Z","title_canon_sha256":"c60bc33522721600b58502f90b3f675d723f01dbde488bbf8a6c0cfedbe4929f"},"schema_version":"1.0","source":{"id":"2108.01701","kind":"arxiv","version":2}},"canonical_sha256":"f03408624f7a1ee6f46d68dd1620516488ac0e2a56da0e12bb347954c462ccd1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"f03408624f7a1ee6f46d68dd1620516488ac0e2a56da0e12bb347954c462ccd1","first_computed_at":"2026-07-05T03:03:30.003231Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T03:03:30.003231Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"OrEHEGUKH7qXALfTpBImNvZ1AIYdjpOhVjm8LcPU9xeKhXsckQji88iM3JVe7s65PAzDoWJnLEFHoYCCiS1vAw==","signature_status":"signed_v1","signed_at":"2026-07-05T03:03:30.003623Z","signed_message":"canonical_sha256_bytes"},"source_id":"2108.01701","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:472226eb8a29e80e5718622585a55d8b4c80fe973750f70f96bf8a1823ea8653","sha256:64ae23a7d9aa0371b4de31b1c65dc963f68e69820d55b008e76167d032e653f3"],"state_sha256":"8c8b7178cd5b16b60053789f5175d914459ca2c85aee2b9db400f6af0df2bbd9"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dMqarLM3uAsiZ/5BG8YQ95fsaC3CT0RGu1A504JxwCebfmZpazVs1uEUWk/RlDHnvAp8XoCx/vr6l29eDLNcDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-04T19:21:47.288300Z","bundle_sha256":"d77d3086111eb24ed0da131abdcab253b4e20acbe880b53c042835a67a6ab621"}}