{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7DZ5EKDP6ZJ4KW3Y7O3LP2L434","short_pith_number":"pith:7DZ5EKDP","schema_version":"1.0","canonical_sha256":"f8f3d2286ff653c55b78fbb6b7e97cdf006da52cc584d887f19c6cbd2433b91b","source":{"kind":"arxiv","id":"2507.07421","version":1},"attestation_state":"computed","paper":{"title":"SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Avijit Mitra, David A. Levy, Emily Druhl, Hong Yu, Jack Tsai, Youxia Zhao, Zonghai Yao","submitted_at":"2025-07-10T04:31:01Z","abstract_excerpt":"Eviction is a significant yet understudied social determinants of health (SDoH), linked to housing instability, unemployment, and mental health. While eviction appears in unstructured electronic health records (EHRs), it is rarely coded in structured fields, limiting downstream applications. We introduce SynthEHR-Eviction, a scalable pipeline combining LLMs, human-in-the-loop annotation, and automated prompt optimization (APO) to extract eviction statuses from clinical notes. Using this pipeline, we created the largest public eviction-related SDoH dataset to date, comprising 14 fine-grained ca"},"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":"2507.07421","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-07-10T04:31:01Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"b963024d9486b150e119799b71f668e6145f2bf67b2e61c18c9f89237fc7c119","abstract_canon_sha256":"eb151d29801a91eee1d8b9be493390a10f9a4002d5a35e8240d23983fe6f6d88"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:34:57.626110Z","signature_b64":"HxKSxCCB5S5CmXEfNL02MegXEipgZS2nl6ti1kLo1K/rVt5XszR7UaqZkixpsHFU2Epu49mu/ByIQ/LpaxrSDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f8f3d2286ff653c55b78fbb6b7e97cdf006da52cc584d887f19c6cbd2433b91b","last_reissued_at":"2026-07-05T11:34:57.625634Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:34:57.625634Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"SynthEHR-Eviction: Enhancing Eviction SDoH Detection with LLM-Augmented Synthetic EHR Data","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CL","authors_text":"Avijit Mitra, David A. Levy, Emily Druhl, Hong Yu, Jack Tsai, Youxia Zhao, Zonghai Yao","submitted_at":"2025-07-10T04:31:01Z","abstract_excerpt":"Eviction is a significant yet understudied social determinants of health (SDoH), linked to housing instability, unemployment, and mental health. While eviction appears in unstructured electronic health records (EHRs), it is rarely coded in structured fields, limiting downstream applications. We introduce SynthEHR-Eviction, a scalable pipeline combining LLMs, human-in-the-loop annotation, and automated prompt optimization (APO) to extract eviction statuses from clinical notes. Using this pipeline, we created the largest public eviction-related SDoH dataset to date, comprising 14 fine-grained ca"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.07421","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/2507.07421/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":"2507.07421","created_at":"2026-07-05T11:34:57.625694+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.07421v1","created_at":"2026-07-05T11:34:57.625694+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.07421","created_at":"2026-07-05T11:34:57.625694+00:00"},{"alias_kind":"pith_short_12","alias_value":"7DZ5EKDP6ZJ4","created_at":"2026-07-05T11:34:57.625694+00:00"},{"alias_kind":"pith_short_16","alias_value":"7DZ5EKDP6ZJ4KW3Y","created_at":"2026-07-05T11:34:57.625694+00:00"},{"alias_kind":"pith_short_8","alias_value":"7DZ5EKDP","created_at":"2026-07-05T11:34:57.625694+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/7DZ5EKDP6ZJ4KW3Y7O3LP2L434","json":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434.json","graph_json":"https://pith.science/api/pith-number/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/graph.json","events_json":"https://pith.science/api/pith-number/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/events.json","paper":"https://pith.science/paper/7DZ5EKDP"},"agent_actions":{"view_html":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434","download_json":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434.json","view_paper":"https://pith.science/paper/7DZ5EKDP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.07421&json=true","fetch_graph":"https://pith.science/api/pith-number/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/graph.json","fetch_events":"https://pith.science/api/pith-number/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/action/storage_attestation","attest_author":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/action/author_attestation","sign_citation":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/action/citation_signature","submit_replication":"https://pith.science/pith/7DZ5EKDP6ZJ4KW3Y7O3LP2L434/action/replication_record"}},"created_at":"2026-07-05T11:34:57.625694+00:00","updated_at":"2026-07-05T11:34:57.625694+00:00"}