{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2019:GDLIBCA4K7SWCZLVE2JR7LZH7K","short_pith_number":"pith:GDLIBCA4","schema_version":"1.0","canonical_sha256":"30d680881c57e561657526931faf27faa08cf1a1a4c2b3db639361d4ee026a14","source":{"kind":"arxiv","id":"1905.05877","version":1},"attestation_state":"computed","paper":{"title":"Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Meliha Yetisgen, Ozlem Uzuner, Thomas H Payne, Wilson Lau","submitted_at":"2019-05-14T23:11:46Z","abstract_excerpt":"Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 567 radiology reports annotated for recommendation information. Our extraction models achieved 0.92 f-score for reco"},"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":"1905.05877","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2019-05-14T23:11:46Z","cross_cats_sorted":[],"title_canon_sha256":"e52271db6a533df1f483148c40d284d099916adb33faa2e10a4091b0df39dd37","abstract_canon_sha256":"46cce509c6c2a7b212931b391a64d3d5728e1858653378823b79a2e1b7d08f87"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:46:08.249086Z","signature_b64":"3i1rS+iNcvYvWc0Rc+3VqsQZAJrm1955GbZBKECMz1Mli1j+PC+hh0t+aGpLDUbvUb0pz14vqsjYrqpqcsTsAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"30d680881c57e561657526931faf27faa08cf1a1a4c2b3db639361d4ee026a14","last_reissued_at":"2026-05-17T23:46:08.248498Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:46:08.248498Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extraction and Analysis of Clinically Important Follow-up Recommendations in a Large Radiology Dataset","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Meliha Yetisgen, Ozlem Uzuner, Thomas H Payne, Wilson Lau","submitted_at":"2019-05-14T23:11:46Z","abstract_excerpt":"Communication of follow-up recommendations when abnormalities are identified on imaging studies is prone to error. In this paper, we present a natural language processing approach based on deep learning to automatically identify clinically important recommendations in radiology reports. Our approach first identifies the recommendation sentences and then extracts reason, test, and time frame of the identified recommendations. To train our extraction models, we created a corpus of 567 radiology reports annotated for recommendation information. Our extraction models achieved 0.92 f-score for reco"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1905.05877","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":""},"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":"1905.05877","created_at":"2026-05-17T23:46:08.248570+00:00"},{"alias_kind":"arxiv_version","alias_value":"1905.05877v1","created_at":"2026-05-17T23:46:08.248570+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1905.05877","created_at":"2026-05-17T23:46:08.248570+00:00"},{"alias_kind":"pith_short_12","alias_value":"GDLIBCA4K7SW","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_16","alias_value":"GDLIBCA4K7SWCZLV","created_at":"2026-05-18T12:33:18.533446+00:00"},{"alias_kind":"pith_short_8","alias_value":"GDLIBCA4","created_at":"2026-05-18T12:33:18.533446+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/GDLIBCA4K7SWCZLVE2JR7LZH7K","json":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K.json","graph_json":"https://pith.science/api/pith-number/GDLIBCA4K7SWCZLVE2JR7LZH7K/graph.json","events_json":"https://pith.science/api/pith-number/GDLIBCA4K7SWCZLVE2JR7LZH7K/events.json","paper":"https://pith.science/paper/GDLIBCA4"},"agent_actions":{"view_html":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K","download_json":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K.json","view_paper":"https://pith.science/paper/GDLIBCA4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1905.05877&json=true","fetch_graph":"https://pith.science/api/pith-number/GDLIBCA4K7SWCZLVE2JR7LZH7K/graph.json","fetch_events":"https://pith.science/api/pith-number/GDLIBCA4K7SWCZLVE2JR7LZH7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K/action/storage_attestation","attest_author":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K/action/author_attestation","sign_citation":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K/action/citation_signature","submit_replication":"https://pith.science/pith/GDLIBCA4K7SWCZLVE2JR7LZH7K/action/replication_record"}},"created_at":"2026-05-17T23:46:08.248570+00:00","updated_at":"2026-05-17T23:46:08.248570+00:00"}