{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:74I5FQGPL2YJ435HNYV7DZTYRN","short_pith_number":"pith:74I5FQGP","schema_version":"1.0","canonical_sha256":"ff11d2c0cf5eb09e6fa76e2bf1e6788b64fcc35e65d44d43ef9053a987bebb28","source":{"kind":"arxiv","id":"2011.04749","version":1},"attestation_state":"computed","paper":{"title":"Longitudinal modeling of MS patient trajectories improves predictions of disability progression","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aysun Soysal, Bart Van Wijmeersch, Cavit Boz, Celia Oreja Guevara, Claudio Solaro, Cristina Ramo-Tello, Daniele Spitaleri, Davide Maimone, Eduardo Aguera-Morales, Edward De Brouwer, Elisabetta Cartechini, Eva Kubala Havrdova, Franco Granella, Francois GrandMaison, Fraser Moore, Gerardo Iuliano, Jeannette Lechner-Scott, Jens Kuhle, Jose Luis Sanchez-Menoyo, Katherine Buzzard, Liesbet Peeters, Ludwig Kappos, Marco Onofrj, Maria Jose Sa, Maria Trojano, Murat Terzi, Oliver Gerlach, Patrizia Sola, Pierre Grammond, Raed Alroughani, Riadh Gouider, Roberto Bergamaschi, Sara Eichau, Serkan Ozakbas, Tamara Castillo Trivio, Thijs Becker, Tomas Kalincik, Tunde Csepany, Vahid Shaygannej, Vincent Van Pesch, Yves Moreau","submitted_at":"2020-11-09T20:48:00Z","abstract_excerpt":"Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited f"},"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":"2011.04749","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.LG","submitted_at":"2020-11-09T20:48:00Z","cross_cats_sorted":[],"title_canon_sha256":"a41bcda5d2f34024984729987f9a15fcdfce256844ea642ba5ff50ebb721d00c","abstract_canon_sha256":"2538be53c628ffed810ffb0144cd4c79a27e564150d88e4a289e03ffd73ed0df"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:50:39.949672Z","signature_b64":"S6Ys6r0mHwJlWlA9MskTLnxwbaMEAB1LuFrlQdrRiFsrcJZCO8iuhilqUiW6P6nPAAIdNtztJ3QGtrj3mGF1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ff11d2c0cf5eb09e6fa76e2bf1e6788b64fcc35e65d44d43ef9053a987bebb28","last_reissued_at":"2026-07-05T01:50:39.949264Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:50:39.949264Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Longitudinal modeling of MS patient trajectories improves predictions of disability progression","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Aysun Soysal, Bart Van Wijmeersch, Cavit Boz, Celia Oreja Guevara, Claudio Solaro, Cristina Ramo-Tello, Daniele Spitaleri, Davide Maimone, Eduardo Aguera-Morales, Edward De Brouwer, Elisabetta Cartechini, Eva Kubala Havrdova, Franco Granella, Francois GrandMaison, Fraser Moore, Gerardo Iuliano, Jeannette Lechner-Scott, Jens Kuhle, Jose Luis Sanchez-Menoyo, Katherine Buzzard, Liesbet Peeters, Ludwig Kappos, Marco Onofrj, Maria Jose Sa, Maria Trojano, Murat Terzi, Oliver Gerlach, Patrizia Sola, Pierre Grammond, Raed Alroughani, Riadh Gouider, Roberto Bergamaschi, Sara Eichau, Serkan Ozakbas, Tamara Castillo Trivio, Thijs Becker, Tomas Kalincik, Tunde Csepany, Vahid Shaygannej, Vincent Van Pesch, Yves Moreau","submitted_at":"2020-11-09T20:48:00Z","abstract_excerpt":"Research in Multiple Sclerosis (MS) has recently focused on extracting knowledge from real-world clinical data sources. This type of data is more abundant than data produced during clinical trials and potentially more informative about real-world clinical practice. However, this comes at the cost of less curated and controlled data sets. In this work, we address the task of optimally extracting information from longitudinal patient data in the real-world setting with a special focus on the sporadic sampling problem. Using the MSBase registry, we show that with machine learning methods suited f"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2011.04749","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/2011.04749/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":"2011.04749","created_at":"2026-07-05T01:50:39.949322+00:00"},{"alias_kind":"arxiv_version","alias_value":"2011.04749v1","created_at":"2026-07-05T01:50:39.949322+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2011.04749","created_at":"2026-07-05T01:50:39.949322+00:00"},{"alias_kind":"pith_short_12","alias_value":"74I5FQGPL2YJ","created_at":"2026-07-05T01:50:39.949322+00:00"},{"alias_kind":"pith_short_16","alias_value":"74I5FQGPL2YJ435H","created_at":"2026-07-05T01:50:39.949322+00:00"},{"alias_kind":"pith_short_8","alias_value":"74I5FQGP","created_at":"2026-07-05T01:50:39.949322+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/74I5FQGPL2YJ435HNYV7DZTYRN","json":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN.json","graph_json":"https://pith.science/api/pith-number/74I5FQGPL2YJ435HNYV7DZTYRN/graph.json","events_json":"https://pith.science/api/pith-number/74I5FQGPL2YJ435HNYV7DZTYRN/events.json","paper":"https://pith.science/paper/74I5FQGP"},"agent_actions":{"view_html":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN","download_json":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN.json","view_paper":"https://pith.science/paper/74I5FQGP","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2011.04749&json=true","fetch_graph":"https://pith.science/api/pith-number/74I5FQGPL2YJ435HNYV7DZTYRN/graph.json","fetch_events":"https://pith.science/api/pith-number/74I5FQGPL2YJ435HNYV7DZTYRN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN/action/storage_attestation","attest_author":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN/action/author_attestation","sign_citation":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN/action/citation_signature","submit_replication":"https://pith.science/pith/74I5FQGPL2YJ435HNYV7DZTYRN/action/replication_record"}},"created_at":"2026-07-05T01:50:39.949322+00:00","updated_at":"2026-07-05T01:50:39.949322+00:00"}