{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:77GDEGLF7YHKTRQMBOKF3BGS6A","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":"34ef961247192387155d0647932900b64ff796899477cb807b58f2a476e82812","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-07T14:45:47Z","title_canon_sha256":"d834e9d2eb50e7b397781c359bec3efa29f3f2269a580a19c319c961822c5cc1"},"schema_version":"1.0","source":{"id":"1910.02830","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1910.02830","created_at":"2026-07-05T00:10:11Z"},{"alias_kind":"arxiv_version","alias_value":"1910.02830v1","created_at":"2026-07-05T00:10:11Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1910.02830","created_at":"2026-07-05T00:10:11Z"},{"alias_kind":"pith_short_12","alias_value":"77GDEGLF7YHK","created_at":"2026-07-05T00:10:11Z"},{"alias_kind":"pith_short_16","alias_value":"77GDEGLF7YHKTRQM","created_at":"2026-07-05T00:10:11Z"},{"alias_kind":"pith_short_8","alias_value":"77GDEGLF","created_at":"2026-07-05T00:10:11Z"}],"graph_snapshots":[{"event_id":"sha256:202c6dfdd123633b9cc5972820a19b2addf60ec1fc67f6d2acdff5b004453e3d","target":"graph","created_at":"2026-07-05T00:10:11Z","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/1910.02830/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Machine-learned diagnosis models have shown promise as medical aides but are trained under a closed-set assumption, i.e. that models will only encounter conditions on which they have been trained. However, it is practically infeasible to obtain sufficient training data for every human condition, and once deployed such models will invariably face previously unseen conditions. We frame machine-learned diagnosis as an open-set learning problem, and study how state-of-the-art approaches compare. Further, we extend our study to a setting where training data is distributed across several healthcare ","authors_text":"Anitha Kannan, David Sontag, Geoffrey J. Tso, Manish Chablani, Namit Katariya, Viraj Prabhu, Xavier Amatriain","cross_cats":["cs.AI","stat.ML"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-07T14:45:47Z","title":"Open Set Medical Diagnosis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1910.02830","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":"sha256:2755cc082138b4043d22f76f6d2df57ed6a853484bfeee7b030f0bd07cf63dd5","target":"record","created_at":"2026-07-05T00:10:11Z","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":"34ef961247192387155d0647932900b64ff796899477cb807b58f2a476e82812","cross_cats_sorted":["cs.AI","stat.ML"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2019-10-07T14:45:47Z","title_canon_sha256":"d834e9d2eb50e7b397781c359bec3efa29f3f2269a580a19c319c961822c5cc1"},"schema_version":"1.0","source":{"id":"1910.02830","kind":"arxiv","version":1}},"canonical_sha256":"ffcc321965fe0ea9c60c0b945d84d2f01d775d23b31b43a0aba1695d3340e99c","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ffcc321965fe0ea9c60c0b945d84d2f01d775d23b31b43a0aba1695d3340e99c","first_computed_at":"2026-07-05T00:10:11.881586Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:10:11.881586Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"IkWXCDJgHHQ38B+nlTLFpnmkMgHfPaK77nhPNf2PcPH/NxZkRTOUCnm0Z3EghtjnbZjLxf+JLVX7Dr12+18CDg==","signature_status":"signed_v1","signed_at":"2026-07-05T00:10:11.881929Z","signed_message":"canonical_sha256_bytes"},"source_id":"1910.02830","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:2755cc082138b4043d22f76f6d2df57ed6a853484bfeee7b030f0bd07cf63dd5","sha256:202c6dfdd123633b9cc5972820a19b2addf60ec1fc67f6d2acdff5b004453e3d"],"state_sha256":"d691f239749838dbf6bc9fdd8114bb2faa49bb24d21066745ebae3b0d59b263f"}