{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:YSOLMUBIWY356NEBJZ7IRHZ3DK","short_pith_number":"pith:YSOLMUBI","schema_version":"1.0","canonical_sha256":"c49cb65028b637df34814e7e889f3b1a99c12e6a23eba137481c6cda43506601","source":{"kind":"arxiv","id":"2607.11656","version":1},"attestation_state":"computed","paper":{"title":"Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.NC","authors_text":"Christelle Schneuwly Diaz, Duy-Cat Can, Duy-Thanh Vu, Gilles Allali, Narmina Baghirova, Oliver Y. Ch\\'en, Philippe Ryvlin","submitted_at":"2026-07-13T15:05:26Z","abstract_excerpt":"Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left unaddressed, these barriers prevent reliable disease modelling and hinder effective clinical evaluation. Conventional imputation strategies introduce systematic bias, distort inter-feature relationships, and yield overconfident predictions, limitations especially consequential in diagnostic settings. Here, we propose NITROGEN, an imputation-free transformer that jointly models within-patient feature dependencies and be"},"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":"2607.11656","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"q-bio.NC","submitted_at":"2026-07-13T15:05:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"3ed086dd52c2cee9ccffac9b819bde391f63355904b845354b7ba54145e0d648","abstract_canon_sha256":"bb60bc74bfd6f21fee73755fa5cec000853b251aaa5fe83d38e76fa59b833dc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-14T02:22:17.234688Z","signature_b64":"En0jnQGsWmO8NirLEegN3+XUkMZdDVK5DnRiK0yx4zFjWoCx1ZnEwP1WGd6E2fcunF1LrW7VbVSVu5Ri16/mDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c49cb65028b637df34814e7e889f3b1a99c12e6a23eba137481c6cda43506601","last_reissued_at":"2026-07-14T02:22:17.233795Z","signature_status":"signed_v1","first_computed_at":"2026-07-14T02:22:17.233795Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Imputation-free transformer learning enables robust Alzheimer's disease prediction and calibrated uncertainty quantification across heterogeneous clinical cohorts","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"q-bio.NC","authors_text":"Christelle Schneuwly Diaz, Duy-Cat Can, Duy-Thanh Vu, Gilles Allali, Narmina Baghirova, Oliver Y. Ch\\'en, Philippe Ryvlin","submitted_at":"2026-07-13T15:05:26Z","abstract_excerpt":"Accurate diagnostic classification and disease-severity prediction for Alzheimer's disease are hampered by the incompleteness and heterogeneity of real-world clinical data. Left unaddressed, these barriers prevent reliable disease modelling and hinder effective clinical evaluation. Conventional imputation strategies introduce systematic bias, distort inter-feature relationships, and yield overconfident predictions, limitations especially consequential in diagnostic settings. Here, we propose NITROGEN, an imputation-free transformer that jointly models within-patient feature dependencies and be"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.11656","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/2607.11656/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":"2607.11656","created_at":"2026-07-14T02:22:17.234272+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.11656v1","created_at":"2026-07-14T02:22:17.234272+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.11656","created_at":"2026-07-14T02:22:17.234272+00:00"},{"alias_kind":"pith_short_12","alias_value":"YSOLMUBIWY35","created_at":"2026-07-14T02:22:17.234272+00:00"},{"alias_kind":"pith_short_16","alias_value":"YSOLMUBIWY356NEB","created_at":"2026-07-14T02:22:17.234272+00:00"},{"alias_kind":"pith_short_8","alias_value":"YSOLMUBI","created_at":"2026-07-14T02:22:17.234272+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/YSOLMUBIWY356NEBJZ7IRHZ3DK","json":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK.json","graph_json":"https://pith.science/api/pith-number/YSOLMUBIWY356NEBJZ7IRHZ3DK/graph.json","events_json":"https://pith.science/api/pith-number/YSOLMUBIWY356NEBJZ7IRHZ3DK/events.json","paper":"https://pith.science/paper/YSOLMUBI"},"agent_actions":{"view_html":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK","download_json":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK.json","view_paper":"https://pith.science/paper/YSOLMUBI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.11656&json=true","fetch_graph":"https://pith.science/api/pith-number/YSOLMUBIWY356NEBJZ7IRHZ3DK/graph.json","fetch_events":"https://pith.science/api/pith-number/YSOLMUBIWY356NEBJZ7IRHZ3DK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK/action/storage_attestation","attest_author":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK/action/author_attestation","sign_citation":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK/action/citation_signature","submit_replication":"https://pith.science/pith/YSOLMUBIWY356NEBJZ7IRHZ3DK/action/replication_record"}},"created_at":"2026-07-14T02:22:17.234272+00:00","updated_at":"2026-07-14T02:22:17.234272+00:00"}