{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:LSN3L4IPUIHVFO7NWB5IYPL5GI","short_pith_number":"pith:LSN3L4IP","schema_version":"1.0","canonical_sha256":"5c9bb5f10fa20f52bbedb07a8c3d7d321208b9b2c6975033262fad32ab0443f7","source":{"kind":"arxiv","id":"2010.09394","version":2},"attestation_state":"computed","paper":{"title":"Knowledge Graph-based Question Answering with Electronic Health Records","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.DB","authors_text":"Edward Choi, Haneol Lee, Jaegul Choo, Junwoo Park, Youngwoo Cho","submitted_at":"2020-10-19T11:31:20Z","abstract_excerpt":"Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine. In this light, QA on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a directed acyclic graph, allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more natur"},"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":"2010.09394","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.DB","submitted_at":"2020-10-19T11:31:20Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"c606cd632f5915a356fa7c080eb6329dd7e447b1ca237f711dfb23753084e188","abstract_canon_sha256":"ebdbb01747d645be8750886c0836b9e0e02bc150660b8ee865e524b315ba8c10"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T03:02:09.249123Z","signature_b64":"LJw7XUXqejdV4G4Sg6LlWMq/4IHF2xcWzrd2CFdmAqFgCGqw5bgCZ3EpCuaRRzFwNy4RHtYiNM2LRQnJakosAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5c9bb5f10fa20f52bbedb07a8c3d7d321208b9b2c6975033262fad32ab0443f7","last_reissued_at":"2026-07-05T03:02:09.248629Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T03:02:09.248629Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Knowledge Graph-based Question Answering with Electronic Health Records","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"cs.DB","authors_text":"Edward Choi, Haneol Lee, Jaegul Choo, Junwoo Park, Youngwoo Cho","submitted_at":"2020-10-19T11:31:20Z","abstract_excerpt":"Question Answering (QA) is a widely-used framework for developing and evaluating an intelligent machine. In this light, QA on Electronic Health Records (EHR), namely EHR QA, can work as a crucial milestone towards developing an intelligent agent in healthcare. EHR data are typically stored in a relational database, which can also be converted to a directed acyclic graph, allowing two approaches for EHR QA: Table-based QA and Knowledge Graph-based QA. We hypothesize that the graph-based approach is more suitable for EHR QA as graphs can represent relations between entities and values more natur"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2010.09394","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/2010.09394/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":"2010.09394","created_at":"2026-07-05T03:02:09.248705+00:00"},{"alias_kind":"arxiv_version","alias_value":"2010.09394v2","created_at":"2026-07-05T03:02:09.248705+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2010.09394","created_at":"2026-07-05T03:02:09.248705+00:00"},{"alias_kind":"pith_short_12","alias_value":"LSN3L4IPUIHV","created_at":"2026-07-05T03:02:09.248705+00:00"},{"alias_kind":"pith_short_16","alias_value":"LSN3L4IPUIHVFO7N","created_at":"2026-07-05T03:02:09.248705+00:00"},{"alias_kind":"pith_short_8","alias_value":"LSN3L4IP","created_at":"2026-07-05T03:02:09.248705+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/LSN3L4IPUIHVFO7NWB5IYPL5GI","json":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI.json","graph_json":"https://pith.science/api/pith-number/LSN3L4IPUIHVFO7NWB5IYPL5GI/graph.json","events_json":"https://pith.science/api/pith-number/LSN3L4IPUIHVFO7NWB5IYPL5GI/events.json","paper":"https://pith.science/paper/LSN3L4IP"},"agent_actions":{"view_html":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI","download_json":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI.json","view_paper":"https://pith.science/paper/LSN3L4IP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2010.09394&json=true","fetch_graph":"https://pith.science/api/pith-number/LSN3L4IPUIHVFO7NWB5IYPL5GI/graph.json","fetch_events":"https://pith.science/api/pith-number/LSN3L4IPUIHVFO7NWB5IYPL5GI/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI/action/storage_attestation","attest_author":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI/action/author_attestation","sign_citation":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI/action/citation_signature","submit_replication":"https://pith.science/pith/LSN3L4IPUIHVFO7NWB5IYPL5GI/action/replication_record"}},"created_at":"2026-07-05T03:02:09.248705+00:00","updated_at":"2026-07-05T03:02:09.248705+00:00"}