{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:TXIDKFR3IJYHADTABI5UURX6PW","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":"a616a2277473372d5615292457b61c933884d81e4433bd8efb79de518944fc68","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2023-09-19T03:50:30Z","title_canon_sha256":"4277e9cd316701c547215706f7310b81d922c24e4b2e8741ba45123c167d2464"},"schema_version":"1.0","source":{"id":"2309.10293","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2309.10293","created_at":"2026-07-05T09:25:33Z"},{"alias_kind":"arxiv_version","alias_value":"2309.10293v3","created_at":"2026-07-05T09:25:33Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2309.10293","created_at":"2026-07-05T09:25:33Z"},{"alias_kind":"pith_short_12","alias_value":"TXIDKFR3IJYH","created_at":"2026-07-05T09:25:33Z"},{"alias_kind":"pith_short_16","alias_value":"TXIDKFR3IJYHADTA","created_at":"2026-07-05T09:25:33Z"},{"alias_kind":"pith_short_8","alias_value":"TXIDKFR3","created_at":"2026-07-05T09:25:33Z"}],"graph_snapshots":[{"event_id":"sha256:d6369b92516f682d6261c2edfd0ab8fc61f64d7400e6d05622f24ade3c14f89a","target":"graph","created_at":"2026-07-05T09:25:33Z","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/2309.10293/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial Intelligence techniques can be used to classify a patient's physical activities and predict vital signs for remote patient monitoring. Regression analysis based on non-linear models like deep learning models has limited explainability due to its black-box nature. This can require decision-makers to make blind leaps of faith based on non-linear model results, especially in healthcare applications. In non-invasive monitoring, patient data from tracking sensors and their predisposing clinical attributes act as input features for predicting future vital signs. Explaining the contributio","authors_text":"Haoran Xie, Juan D. Velasquez, Lin Li, Niall Higgins, Thanveer Shaik, Xiaohui Tao","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2023-09-19T03:50:30Z","title":"QXAI: Explainable AI Framework for Quantitative Analysis in Patient Monitoring Systems"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2309.10293","kind":"arxiv","version":3},"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:da4cd6c709c43b8a24a4c318ca8bea1ece91ae8404ec3ee7179acaab767829af","target":"record","created_at":"2026-07-05T09:25:33Z","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":"a616a2277473372d5615292457b61c933884d81e4433bd8efb79de518944fc68","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.AI","submitted_at":"2023-09-19T03:50:30Z","title_canon_sha256":"4277e9cd316701c547215706f7310b81d922c24e4b2e8741ba45123c167d2464"},"schema_version":"1.0","source":{"id":"2309.10293","kind":"arxiv","version":3}},"canonical_sha256":"9dd035163b4270700e600a3b4a46fe7dbaecd257d0fc2e38e76890a544ee1ae9","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9dd035163b4270700e600a3b4a46fe7dbaecd257d0fc2e38e76890a544ee1ae9","first_computed_at":"2026-07-05T09:25:33.455527Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T09:25:33.455527Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"5h56ZTFR5/cd85C/ozmtIHFQmwKRELjYBPmnAntbuahVnFCnVKL++EPSpEIRPC1ovgl75WIgyXdy+2+jJaECDA==","signature_status":"signed_v1","signed_at":"2026-07-05T09:25:33.456004Z","signed_message":"canonical_sha256_bytes"},"source_id":"2309.10293","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:da4cd6c709c43b8a24a4c318ca8bea1ece91ae8404ec3ee7179acaab767829af","sha256:d6369b92516f682d6261c2edfd0ab8fc61f64d7400e6d05622f24ade3c14f89a"],"state_sha256":"ba83036e794b0d612972b6d77b8f2fd7778aa4fa82aae599dd2ff85431771bd9"}