{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:O7BQID5HYGOBACR4R5XSRFYIBA","short_pith_number":"pith:O7BQID5H","schema_version":"1.0","canonical_sha256":"77c3040fa7c19c100a3c8f6f28970808263b08d2f8a97c6dabb480d29b62c4c5","source":{"kind":"arxiv","id":"2405.14567","version":3},"attestation_state":"computed","paper":{"title":"EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adibvafa Fallahpour, Amrit Krishnan, Arash Afkanpour, Mahshid Alinoori, Wenqian Ye, Xu Cao","submitted_at":"2024-05-23T13:43:29Z","abstract_excerpt":"Transformers have significantly advanced the modeling of Electronic Health Records (EHR), yet their deployment in real-world healthcare is limited by several key challenges. Firstly, the quadratic computational cost and insufficient context length of these models hinder hospitals' ability in processing the extensive medical histories typical in EHR data. Additionally, existing models employ separate finetuning for each clinical task, complicating maintenance in healthcare environments. Moreover, these models focus exclusively on either clinical prediction or EHR forecasting, lacking proficienc"},"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":"2405.14567","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-05-23T13:43:29Z","cross_cats_sorted":[],"title_canon_sha256":"517f679db6233e3e4d0903b5121eb3c4eeee4e109c3b536515c24e6e155a88c3","abstract_canon_sha256":"5de5e012c63447a563af08134a504fbdf8e01742f15ab39ee6f67ade29788bbf"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:35:32.973108Z","signature_b64":"d3GPqdbtOEOEpsZHFDNu3Qf93dkNPKesHeyHp+ByIVIyUCJcRkQpXLAtg55aCCUraenu7zlqEuknRNquJ5tUDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"77c3040fa7c19c100a3c8f6f28970808263b08d2f8a97c6dabb480d29b62c4c5","last_reissued_at":"2026-07-05T09:35:32.972680Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:35:32.972680Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"EHRMamba: Towards Generalizable and Scalable Foundation Models for Electronic Health Records","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Adibvafa Fallahpour, Amrit Krishnan, Arash Afkanpour, Mahshid Alinoori, Wenqian Ye, Xu Cao","submitted_at":"2024-05-23T13:43:29Z","abstract_excerpt":"Transformers have significantly advanced the modeling of Electronic Health Records (EHR), yet their deployment in real-world healthcare is limited by several key challenges. Firstly, the quadratic computational cost and insufficient context length of these models hinder hospitals' ability in processing the extensive medical histories typical in EHR data. Additionally, existing models employ separate finetuning for each clinical task, complicating maintenance in healthcare environments. Moreover, these models focus exclusively on either clinical prediction or EHR forecasting, lacking proficienc"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.14567","kind":"arxiv","version":3},"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/2405.14567/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":"2405.14567","created_at":"2026-07-05T09:35:32.972734+00:00"},{"alias_kind":"arxiv_version","alias_value":"2405.14567v3","created_at":"2026-07-05T09:35:32.972734+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.14567","created_at":"2026-07-05T09:35:32.972734+00:00"},{"alias_kind":"pith_short_12","alias_value":"O7BQID5HYGOB","created_at":"2026-07-05T09:35:32.972734+00:00"},{"alias_kind":"pith_short_16","alias_value":"O7BQID5HYGOBACR4","created_at":"2026-07-05T09:35:32.972734+00:00"},{"alias_kind":"pith_short_8","alias_value":"O7BQID5H","created_at":"2026-07-05T09:35:32.972734+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":11,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.03543","citing_title":"D2MDT: Department-aware Multidisciplinary Team Consultation with Deliberation for Efficient Clinical Prediction","ref_index":84,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28623","citing_title":"Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10840","citing_title":"Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.17765","citing_title":"AURORA: Contextual Orthogonalization for Geometric Representation Learning in Healthcare Foundation Models","ref_index":19,"is_internal_anchor":false},{"citing_arxiv_id":"2604.04175","citing_title":"Uncertainty-Aware Foundation Models for Clinical Data","ref_index":33,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10840","citing_title":"Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.12335","citing_title":"EHR-RAGp: Retrieval-Augmented Prototype-Guided Foundation Model for Electronic Health Records","ref_index":10,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08685","citing_title":"Event Fields: Learning Latent Event Structure for Waveform Foundation Models","ref_index":17,"is_internal_anchor":false},{"citing_arxiv_id":"2605.09765","citing_title":"WISTERIA: Learning Clinical Representations from Noisy Supervision via Multi-View Consistency in Electronic Health Records","ref_index":18,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10840","citing_title":"Clin-JEPA: A Multi-Phase Co-Training Framework for Joint-Embedding Predictive Pretraining on EHR Patient Trajectories","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2604.16775","citing_title":"Representation Before Training: A Fixed-Budget Benchmark for Generative Medical Event Models","ref_index":11,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA","json":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA.json","graph_json":"https://pith.science/api/pith-number/O7BQID5HYGOBACR4R5XSRFYIBA/graph.json","events_json":"https://pith.science/api/pith-number/O7BQID5HYGOBACR4R5XSRFYIBA/events.json","paper":"https://pith.science/paper/O7BQID5H"},"agent_actions":{"view_html":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA","download_json":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA.json","view_paper":"https://pith.science/paper/O7BQID5H","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2405.14567&json=true","fetch_graph":"https://pith.science/api/pith-number/O7BQID5HYGOBACR4R5XSRFYIBA/graph.json","fetch_events":"https://pith.science/api/pith-number/O7BQID5HYGOBACR4R5XSRFYIBA/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA/action/timestamp_anchor","attest_storage":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA/action/storage_attestation","attest_author":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA/action/author_attestation","sign_citation":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA/action/citation_signature","submit_replication":"https://pith.science/pith/O7BQID5HYGOBACR4R5XSRFYIBA/action/replication_record"}},"created_at":"2026-07-05T09:35:32.972734+00:00","updated_at":"2026-07-05T09:35:32.972734+00:00"}