{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:SG3XEVPTW7ATVBM4KTYTHOPO7K","short_pith_number":"pith:SG3XEVPT","schema_version":"1.0","canonical_sha256":"91b77255f3b7c13a859c54f133b9eefa9386c4151db384ac5a71507ddba9757f","source":{"kind":"arxiv","id":"2606.12701","version":1},"attestation_state":"computed","paper":{"title":"Bayesian machine learning approach for recurrent events studies using Soft Bayesian Additive Regression Trees (SBART)","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Antonio Linero, Debajyoti Sinha, MengXing Chen","submitted_at":"2026-06-10T21:43:57Z","abstract_excerpt":"Recurrent event data frequently arise in biomedical studies, where individuals may experience multiple recurrences of the same type of events, such as recurrent hospitalizations. This article introduces a nonparametric method for recurrent events under a Bayesian ensemble learning framework, called Soft Bayesian Additive Regression Trees (SBART), which combines multiple soft decision trees to achieve high predictive accuracy and a smooth estimator of the underlying intensity of the recurrent events. The proposed model represents the conditional intensity function of the non-homogeneous Poisson"},"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":"2606.12701","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"stat.ME","submitted_at":"2026-06-10T21:43:57Z","cross_cats_sorted":[],"title_canon_sha256":"488001770ad8768a370a6d2da9234a345a7c20c9553bc6211b2441b4da53a2df","abstract_canon_sha256":"5a476da723ab2926ac53ee8ade6ed80eabdeb9993d9d83650ea56b3cfccc8c12"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-12T01:08:46.206407Z","signature_b64":"+wD8UwNl7n0HN04gMnwVNXM3wLM7FSBg6lItZj5MryhTo80Gq+jsokcDelVljKMqRcGw58yU1dktwTRHk5juAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"91b77255f3b7c13a859c54f133b9eefa9386c4151db384ac5a71507ddba9757f","last_reissued_at":"2026-06-12T01:08:46.205637Z","signature_status":"signed_v1","first_computed_at":"2026-06-12T01:08:46.205637Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Bayesian machine learning approach for recurrent events studies using Soft Bayesian Additive Regression Trees (SBART)","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"stat.ME","authors_text":"Antonio Linero, Debajyoti Sinha, MengXing Chen","submitted_at":"2026-06-10T21:43:57Z","abstract_excerpt":"Recurrent event data frequently arise in biomedical studies, where individuals may experience multiple recurrences of the same type of events, such as recurrent hospitalizations. This article introduces a nonparametric method for recurrent events under a Bayesian ensemble learning framework, called Soft Bayesian Additive Regression Trees (SBART), which combines multiple soft decision trees to achieve high predictive accuracy and a smooth estimator of the underlying intensity of the recurrent events. The proposed model represents the conditional intensity function of the non-homogeneous Poisson"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.12701","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/2606.12701/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":"2606.12701","created_at":"2026-06-12T01:08:46.205747+00:00"},{"alias_kind":"arxiv_version","alias_value":"2606.12701v1","created_at":"2026-06-12T01:08:46.205747+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.12701","created_at":"2026-06-12T01:08:46.205747+00:00"},{"alias_kind":"pith_short_12","alias_value":"SG3XEVPTW7AT","created_at":"2026-06-12T01:08:46.205747+00:00"},{"alias_kind":"pith_short_16","alias_value":"SG3XEVPTW7ATVBM4","created_at":"2026-06-12T01:08:46.205747+00:00"},{"alias_kind":"pith_short_8","alias_value":"SG3XEVPT","created_at":"2026-06-12T01:08:46.205747+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/SG3XEVPTW7ATVBM4KTYTHOPO7K","json":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K.json","graph_json":"https://pith.science/api/pith-number/SG3XEVPTW7ATVBM4KTYTHOPO7K/graph.json","events_json":"https://pith.science/api/pith-number/SG3XEVPTW7ATVBM4KTYTHOPO7K/events.json","paper":"https://pith.science/paper/SG3XEVPT"},"agent_actions":{"view_html":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K","download_json":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K.json","view_paper":"https://pith.science/paper/SG3XEVPT","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2606.12701&json=true","fetch_graph":"https://pith.science/api/pith-number/SG3XEVPTW7ATVBM4KTYTHOPO7K/graph.json","fetch_events":"https://pith.science/api/pith-number/SG3XEVPTW7ATVBM4KTYTHOPO7K/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K/action/storage_attestation","attest_author":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K/action/author_attestation","sign_citation":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K/action/citation_signature","submit_replication":"https://pith.science/pith/SG3XEVPTW7ATVBM4KTYTHOPO7K/action/replication_record"}},"created_at":"2026-06-12T01:08:46.205747+00:00","updated_at":"2026-06-12T01:08:46.205747+00:00"}