{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:QTIX52MH7IM5EG6MSY4EXCQDXX","short_pith_number":"pith:QTIX52MH","canonical_record":{"source":{"id":"2606.28623","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T21:45:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"62b1e3042301f8dbc455ac0d42280b95fc1e1092b5130e78295997c1e383c1e3","abstract_canon_sha256":"ad47c74f6409807b53f0d344f9cc90db79b2dffe013815b6b7e9d7ebca5778f0"},"schema_version":"1.0"},"canonical_sha256":"84d17ee987fa19d21bcc96384b8a03bdc693f9dbf627691a55d7b0e02e6f886f","source":{"kind":"arxiv","id":"2606.28623","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.28623","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"arxiv_version","alias_value":"2606.28623v1","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28623","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"pith_short_12","alias_value":"QTIX52MH7IM5","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"pith_short_16","alias_value":"QTIX52MH7IM5EG6M","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"pith_short_8","alias_value":"QTIX52MH","created_at":"2026-06-30T00:15:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:QTIX52MH7IM5EG6MSY4EXCQDXX","target":"record","payload":{"canonical_record":{"source":{"id":"2606.28623","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T21:45:07Z","cross_cats_sorted":["stat.ML"],"title_canon_sha256":"62b1e3042301f8dbc455ac0d42280b95fc1e1092b5130e78295997c1e383c1e3","abstract_canon_sha256":"ad47c74f6409807b53f0d344f9cc90db79b2dffe013815b6b7e9d7ebca5778f0"},"schema_version":"1.0"},"canonical_sha256":"84d17ee987fa19d21bcc96384b8a03bdc693f9dbf627691a55d7b0e02e6f886f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-30T00:15:20.843785Z","signature_b64":"B8styqdnfzExQIBpKyCca05zDZXUVy/7d1aYw+B9uaxPL7YeCFYX4MsCInzCSJ5+PPSDtcElrtJE9ABpeWY4Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"84d17ee987fa19d21bcc96384b8a03bdc693f9dbf627691a55d7b0e02e6f886f","last_reissued_at":"2026-06-30T00:15:20.843423Z","signature_status":"signed_v1","first_computed_at":"2026-06-30T00:15:20.843423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2606.28623","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T00:15:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XHnjEC9VsS4rbSAB76kWMfczIh6goJKZoLKaJdZjQTtfNef9Vi33+ZHEZqAGhbH59pBAae+Uw0OciSgbsHwQDQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T22:37:03.726759Z"},"content_sha256":"4db329386aa47f59acd2e9973da6b9f95c78012a06bc28a36afec3f0565f6f1e","schema_version":"1.0","event_id":"sha256:4db329386aa47f59acd2e9973da6b9f95c78012a06bc28a36afec3f0565f6f1e"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:QTIX52MH7IM5EG6MSY4EXCQDXX","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["stat.ML"],"primary_cat":"cs.LG","authors_text":"Md Mozaharul Mottalib, Rahmatollah Beheshti","submitted_at":"2026-06-26T21:45:07Z","abstract_excerpt":"Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on the available labels and subtype patients based"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28623","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/2606.28623/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-06-30T00:15:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qeH7ZjsmO8AD/Uvdf4BERyXEHt021xmdcilcc+JYjFr0q/yqaxovVLK4PFsuy7DPoA0LKRGqOyVzQuQ1JLnkDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T22:37:03.727160Z"},"content_sha256":"071bda22b5aed454033c2789fc047357f3aeca5331814cb98dbdb2e4e6f66ef0","schema_version":"1.0","event_id":"sha256:071bda22b5aed454033c2789fc047357f3aeca5331814cb98dbdb2e4e6f66ef0"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QTIX52MH7IM5EG6MSY4EXCQDXX/bundle.json","state_url":"https://pith.science/pith/QTIX52MH7IM5EG6MSY4EXCQDXX/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QTIX52MH7IM5EG6MSY4EXCQDXX/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-02T22:37:03Z","links":{"resolver":"https://pith.science/pith/QTIX52MH7IM5EG6MSY4EXCQDXX","bundle":"https://pith.science/pith/QTIX52MH7IM5EG6MSY4EXCQDXX/bundle.json","state":"https://pith.science/pith/QTIX52MH7IM5EG6MSY4EXCQDXX/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QTIX52MH7IM5EG6MSY4EXCQDXX/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:QTIX52MH7IM5EG6MSY4EXCQDXX","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"ad47c74f6409807b53f0d344f9cc90db79b2dffe013815b6b7e9d7ebca5778f0","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T21:45:07Z","title_canon_sha256":"62b1e3042301f8dbc455ac0d42280b95fc1e1092b5130e78295997c1e383c1e3"},"schema_version":"1.0","source":{"id":"2606.28623","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2606.28623","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"arxiv_version","alias_value":"2606.28623v1","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2606.28623","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"pith_short_12","alias_value":"QTIX52MH7IM5","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"pith_short_16","alias_value":"QTIX52MH7IM5EG6M","created_at":"2026-06-30T00:15:20Z"},{"alias_kind":"pith_short_8","alias_value":"QTIX52MH","created_at":"2026-06-30T00:15:20Z"}],"graph_snapshots":[{"event_id":"sha256:071bda22b5aed454033c2789fc047357f3aeca5331814cb98dbdb2e4e6f66ef0","target":"graph","created_at":"2026-06-30T00:15:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2606.28623/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Effective sub-typing (also known as grouping or clustering) of patients using their electronic health record (EHR) data can greatly inform precision medicine efforts. However, subtyping temporal EHR datasets is known to be challenging due to inherent EHR issues, including complexity and irregularity. In this study, we propose a self-supervised Mamba-based model that learns effective EHR representations and enables enhanced patient subtyping. We evaluate the proposed model on public and private real-world EHR datasets to classify the data based on the available labels and subtype patients based","authors_text":"Md Mozaharul Mottalib, Rahmatollah Beheshti","cross_cats":["stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T21:45:07Z","title":"Improving Patient Subtyping on Longitudinal Data using Representations from Mamba-based Architecture"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2606.28623","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:4db329386aa47f59acd2e9973da6b9f95c78012a06bc28a36afec3f0565f6f1e","target":"record","created_at":"2026-06-30T00:15:20Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"ad47c74f6409807b53f0d344f9cc90db79b2dffe013815b6b7e9d7ebca5778f0","cross_cats_sorted":["stat.ML"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2026-06-26T21:45:07Z","title_canon_sha256":"62b1e3042301f8dbc455ac0d42280b95fc1e1092b5130e78295997c1e383c1e3"},"schema_version":"1.0","source":{"id":"2606.28623","kind":"arxiv","version":1}},"canonical_sha256":"84d17ee987fa19d21bcc96384b8a03bdc693f9dbf627691a55d7b0e02e6f886f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"84d17ee987fa19d21bcc96384b8a03bdc693f9dbf627691a55d7b0e02e6f886f","first_computed_at":"2026-06-30T00:15:20.843423Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-06-30T00:15:20.843423Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"B8styqdnfzExQIBpKyCca05zDZXUVy/7d1aYw+B9uaxPL7YeCFYX4MsCInzCSJ5+PPSDtcElrtJE9ABpeWY4Cg==","signature_status":"signed_v1","signed_at":"2026-06-30T00:15:20.843785Z","signed_message":"canonical_sha256_bytes"},"source_id":"2606.28623","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4db329386aa47f59acd2e9973da6b9f95c78012a06bc28a36afec3f0565f6f1e","sha256:071bda22b5aed454033c2789fc047357f3aeca5331814cb98dbdb2e4e6f66ef0"],"state_sha256":"5deeef11845808fd8b095253078437532e3eec7137f6e682d1fd1505a03722c4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sms6dumZxyUwLQA0OlgAqC2uN3QyN+TFNKE27N7DviYZr0M3DqeDU+vcCwNBogfzw0eNOmzF1jJ43XOw5IVPDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T22:37:03.729323Z","bundle_sha256":"1ac7dc91fb1ac8a360aae06943d06e65394ed622a29e87b35246d491e8dd5f77"}}