{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2021:54GXJ7OOQLIKVWEFVJKWKXTMHB","short_pith_number":"pith:54GXJ7OO","schema_version":"1.0","canonical_sha256":"ef0d74fdce82d0aad885aa55655e6c384c68cbd78060525f769498458de55ea3","source":{"kind":"arxiv","id":"2105.09428","version":1},"attestation_state":"computed","paper":{"title":"Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Ancil Crayton, Caroline Trier, Chuhong Lahlou, Evan Willett","submitted_at":"2021-05-19T22:39:15Z","abstract_excerpt":"In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models. Recently, modern language models have played key roles in a number of health related tasks. This paper presents, to the best of our knowledge, the first application of these models to patient readmission prediction. To facilitate this, we create a dataset of 1.2 million medical history samples derived from the Limited Dataset (LDS) i"},"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":"2105.09428","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2021-05-19T22:39:15Z","cross_cats_sorted":["cs.AI","cs.CL"],"title_canon_sha256":"9aa1b286ecdba7fe5a935a5709fe98ffb432a5f20b276c3da2dc38dea125f5c3","abstract_canon_sha256":"4c387fd30ed24f8fa449161e9593a958d2b21cdb1c9a8b39225713836838a051"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:41:52.556227Z","signature_b64":"Qh7rO9jyJ36U0zoxtc3hTaQnp58LOeaQ1gUTMyriNH3dwbWNukM+f5D/FfzAmQ5fn1Z9NQkcHMhKwTIeLVtfCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ef0d74fdce82d0aad885aa55655e6c384c68cbd78060525f769498458de55ea3","last_reissued_at":"2026-07-05T02:41:52.555748Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:41:52.555748Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Explainable Health Risk Predictor with Transformer-based Medicare Claim Encoder","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CL"],"primary_cat":"cs.LG","authors_text":"Ancil Crayton, Caroline Trier, Chuhong Lahlou, Evan Willett","submitted_at":"2021-05-19T22:39:15Z","abstract_excerpt":"In 2019, The Centers for Medicare and Medicaid Services (CMS) launched an Artificial Intelligence (AI) Health Outcomes Challenge seeking solutions to predict risk in value-based care for incorporation into CMS Innovation Center payment and service delivery models. Recently, modern language models have played key roles in a number of health related tasks. This paper presents, to the best of our knowledge, the first application of these models to patient readmission prediction. To facilitate this, we create a dataset of 1.2 million medical history samples derived from the Limited Dataset (LDS) i"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.09428","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/2105.09428/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":"2105.09428","created_at":"2026-07-05T02:41:52.555811+00:00"},{"alias_kind":"arxiv_version","alias_value":"2105.09428v1","created_at":"2026-07-05T02:41:52.555811+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.09428","created_at":"2026-07-05T02:41:52.555811+00:00"},{"alias_kind":"pith_short_12","alias_value":"54GXJ7OOQLIK","created_at":"2026-07-05T02:41:52.555811+00:00"},{"alias_kind":"pith_short_16","alias_value":"54GXJ7OOQLIKVWEF","created_at":"2026-07-05T02:41:52.555811+00:00"},{"alias_kind":"pith_short_8","alias_value":"54GXJ7OO","created_at":"2026-07-05T02:41:52.555811+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.24619","citing_title":"Interpretable phenotyping of Heart Failure patients with Dutch discharge letters","ref_index":43,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB","json":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB.json","graph_json":"https://pith.science/api/pith-number/54GXJ7OOQLIKVWEFVJKWKXTMHB/graph.json","events_json":"https://pith.science/api/pith-number/54GXJ7OOQLIKVWEFVJKWKXTMHB/events.json","paper":"https://pith.science/paper/54GXJ7OO"},"agent_actions":{"view_html":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB","download_json":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB.json","view_paper":"https://pith.science/paper/54GXJ7OO","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2105.09428&json=true","fetch_graph":"https://pith.science/api/pith-number/54GXJ7OOQLIKVWEFVJKWKXTMHB/graph.json","fetch_events":"https://pith.science/api/pith-number/54GXJ7OOQLIKVWEFVJKWKXTMHB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB/action/storage_attestation","attest_author":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB/action/author_attestation","sign_citation":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB/action/citation_signature","submit_replication":"https://pith.science/pith/54GXJ7OOQLIKVWEFVJKWKXTMHB/action/replication_record"}},"created_at":"2026-07-05T02:41:52.555811+00:00","updated_at":"2026-07-05T02:41:52.555811+00:00"}