{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:YXN32QGSF5S5UI2B2IG5N7RLIS","short_pith_number":"pith:YXN32QGS","schema_version":"1.0","canonical_sha256":"c5dbbd40d22f65da2341d20dd6fe2b449b6709a85c87281578e12a9b1cf882a2","source":{"kind":"arxiv","id":"2504.17360","version":1},"attestation_state":"computed","paper":{"title":"PatientDx: Merging Large Language Models for Protecting Data-Privacy in Healthcare","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Christine Damase-Michel, Jesus Lovon (IRIT-IRIS), Jose G. Moreno (IRIT-IRIS), Lynda Tamine (IRIT-IRIS), M'Rick Robin-Charlet (UT3)","submitted_at":"2025-04-24T08:21:04Z","abstract_excerpt":"Fine-tuning of Large Language Models (LLMs) has become the default practice for improving model performance on a given task. However, performance improvement comes at the cost of training on vast amounts of annotated data which could be sensitive leading to significant data privacy concerns. In particular, the healthcare domain is one of the most sensitive domains exposed to data privacy issues. In this paper, we present PatientDx, a framework of model merging that allows the design of effective LLMs for health-predictive tasks without requiring fine-tuning nor adaptation on patient data. Our "},"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":"2504.17360","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CL","submitted_at":"2025-04-24T08:21:04Z","cross_cats_sorted":[],"title_canon_sha256":"862a73a46294440cf6527a768532b97030166d40b60bef58d5bda416180bb9d1","abstract_canon_sha256":"09b1f574489e471b861593c8b1a6c2822949dc2ddda91cccc65e815dcf7f6c0b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:53:27.471449Z","signature_b64":"HBpP1/ZVp3iAceZOXbe+b+AY9bbVYfbMJtjHowpem0Hfan63fCc1ZjLbA6rEHXkqoDCxrTrMxqtTK5zzWYd1BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c5dbbd40d22f65da2341d20dd6fe2b449b6709a85c87281578e12a9b1cf882a2","last_reissued_at":"2026-07-05T10:53:27.470918Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:53:27.470918Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PatientDx: Merging Large Language Models for Protecting Data-Privacy in Healthcare","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CL","authors_text":"Christine Damase-Michel, Jesus Lovon (IRIT-IRIS), Jose G. Moreno (IRIT-IRIS), Lynda Tamine (IRIT-IRIS), M'Rick Robin-Charlet (UT3)","submitted_at":"2025-04-24T08:21:04Z","abstract_excerpt":"Fine-tuning of Large Language Models (LLMs) has become the default practice for improving model performance on a given task. However, performance improvement comes at the cost of training on vast amounts of annotated data which could be sensitive leading to significant data privacy concerns. In particular, the healthcare domain is one of the most sensitive domains exposed to data privacy issues. In this paper, we present PatientDx, a framework of model merging that allows the design of effective LLMs for health-predictive tasks without requiring fine-tuning nor adaptation on patient data. Our "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.17360","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/2504.17360/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":"2504.17360","created_at":"2026-07-05T10:53:27.470984+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.17360v1","created_at":"2026-07-05T10:53:27.470984+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.17360","created_at":"2026-07-05T10:53:27.470984+00:00"},{"alias_kind":"pith_short_12","alias_value":"YXN32QGSF5S5","created_at":"2026-07-05T10:53:27.470984+00:00"},{"alias_kind":"pith_short_16","alias_value":"YXN32QGSF5S5UI2B","created_at":"2026-07-05T10:53:27.470984+00:00"},{"alias_kind":"pith_short_8","alias_value":"YXN32QGS","created_at":"2026-07-05T10:53:27.470984+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/YXN32QGSF5S5UI2B2IG5N7RLIS","json":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS.json","graph_json":"https://pith.science/api/pith-number/YXN32QGSF5S5UI2B2IG5N7RLIS/graph.json","events_json":"https://pith.science/api/pith-number/YXN32QGSF5S5UI2B2IG5N7RLIS/events.json","paper":"https://pith.science/paper/YXN32QGS"},"agent_actions":{"view_html":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS","download_json":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS.json","view_paper":"https://pith.science/paper/YXN32QGS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.17360&json=true","fetch_graph":"https://pith.science/api/pith-number/YXN32QGSF5S5UI2B2IG5N7RLIS/graph.json","fetch_events":"https://pith.science/api/pith-number/YXN32QGSF5S5UI2B2IG5N7RLIS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS/action/storage_attestation","attest_author":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS/action/author_attestation","sign_citation":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS/action/citation_signature","submit_replication":"https://pith.science/pith/YXN32QGSF5S5UI2B2IG5N7RLIS/action/replication_record"}},"created_at":"2026-07-05T10:53:27.470984+00:00","updated_at":"2026-07-05T10:53:27.470984+00:00"}