{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2GS6OA2IORS2DB76GGSJTYX2IG","short_pith_number":"pith:2GS6OA2I","schema_version":"1.0","canonical_sha256":"d1a5e703487465a187fe31a499e2fa41ab94a573560ea7b0b77c3f01d69f621b","source":{"kind":"arxiv","id":"2409.03180","version":1},"attestation_state":"computed","paper":{"title":"Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexandru Bogdan, Hersh Sagreiya, Negar Orangi-Fard","submitted_at":"2024-09-05T02:14:31Z","abstract_excerpt":"Respiratory diseases impose a significant burden on global health, with current diagnostic and management practices primarily reliant on specialist clinical testing. This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment for patients undergoing continuous positive airway pressure (CPAP) therapy. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements under three breathing conditions: normal, panting, and deep breathing. Various machine"},"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":"2409.03180","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2024-09-05T02:14:31Z","cross_cats_sorted":[],"title_canon_sha256":"7fd4c3600fd42e275b6f7f59e1e51d7d037f37244aac7cfedf78e23b15674091","abstract_canon_sha256":"af4a015563c1dfb26c785625b25c9b202b02f1d004e44688a5ddd9d915520e3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:03:27.169417Z","signature_b64":"P/GMJKCiLm3GG0NoXUsLp+jDBXSKhS54tWW62Wb/qBfUYi01VwTws866RJztNPbwqBtfJvJ+1ze8tYS47JChCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d1a5e703487465a187fe31a499e2fa41ab94a573560ea7b0b77c3f01d69f621b","last_reissued_at":"2026-07-05T09:03:27.168981Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:03:27.168981Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning-based algorithms for at-home respiratory disease monitoring and respiratory assessment","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Alexandru Bogdan, Hersh Sagreiya, Negar Orangi-Fard","submitted_at":"2024-09-05T02:14:31Z","abstract_excerpt":"Respiratory diseases impose a significant burden on global health, with current diagnostic and management practices primarily reliant on specialist clinical testing. This work aims to develop machine learning-based algorithms to facilitate at-home respiratory disease monitoring and assessment for patients undergoing continuous positive airway pressure (CPAP) therapy. Data were collected from 30 healthy adults, encompassing respiratory pressure, flow, and dynamic thoraco-abdominal circumferential measurements under three breathing conditions: normal, panting, and deep breathing. Various machine"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.03180","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/2409.03180/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":"2409.03180","created_at":"2026-07-05T09:03:27.169046+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.03180v1","created_at":"2026-07-05T09:03:27.169046+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.03180","created_at":"2026-07-05T09:03:27.169046+00:00"},{"alias_kind":"pith_short_12","alias_value":"2GS6OA2IORS2","created_at":"2026-07-05T09:03:27.169046+00:00"},{"alias_kind":"pith_short_16","alias_value":"2GS6OA2IORS2DB76","created_at":"2026-07-05T09:03:27.169046+00:00"},{"alias_kind":"pith_short_8","alias_value":"2GS6OA2I","created_at":"2026-07-05T09:03:27.169046+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/2GS6OA2IORS2DB76GGSJTYX2IG","json":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG.json","graph_json":"https://pith.science/api/pith-number/2GS6OA2IORS2DB76GGSJTYX2IG/graph.json","events_json":"https://pith.science/api/pith-number/2GS6OA2IORS2DB76GGSJTYX2IG/events.json","paper":"https://pith.science/paper/2GS6OA2I"},"agent_actions":{"view_html":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG","download_json":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG.json","view_paper":"https://pith.science/paper/2GS6OA2I","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.03180&json=true","fetch_graph":"https://pith.science/api/pith-number/2GS6OA2IORS2DB76GGSJTYX2IG/graph.json","fetch_events":"https://pith.science/api/pith-number/2GS6OA2IORS2DB76GGSJTYX2IG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG/action/storage_attestation","attest_author":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG/action/author_attestation","sign_citation":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG/action/citation_signature","submit_replication":"https://pith.science/pith/2GS6OA2IORS2DB76GGSJTYX2IG/action/replication_record"}},"created_at":"2026-07-05T09:03:27.169046+00:00","updated_at":"2026-07-05T09:03:27.169046+00:00"}