{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:22CIB7O6Q6FSVUOK36BOHZTRIQ","short_pith_number":"pith:22CIB7O6","schema_version":"1.0","canonical_sha256":"d68480fdde878b2ad1cadf82e3e6714420d4b17d379b1b66847afbde97165980","source":{"kind":"arxiv","id":"2508.18688","version":2},"attestation_state":"computed","paper":{"title":"End to End Autoencoder MLP Framework for Sepsis Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bin Yi, Di Wu, Hejiang Cai, Ji Xu, Xiang Liu, Xin Shu, Yiziting Zhu, Yujie Li","submitted_at":"2025-08-26T05:22:48Z","abstract_excerpt":"Sepsis is a life threatening condition that requires timely detection in intensive care settings. Traditional machine learning approaches, including Naive Bayes, Support Vector Machine (SVM), Random Forest, and XGBoost, often rely on manual feature engineering and struggle with irregular, incomplete time-series data commonly present in electronic health records. We introduce an end-to-end deep learning framework integrating an unsupervised autoencoder for automatic feature extraction with a multilayer perceptron classifier for binary sepsis risk prediction. To enhance clinical applicability, w"},"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":"2508.18688","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-08-26T05:22:48Z","cross_cats_sorted":[],"title_canon_sha256":"c6d4af6cbc42095772cd7358b7ef32a264b50ec870508848484e49bbedc93fa7","abstract_canon_sha256":"b62736b6d002c7d73e436a3e144ca1d171f4063d2123058a9aae36f35cdaa1c5"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T12:03:32.755704Z","signature_b64":"HG8VE5mUFOV6K5wqv0CQ5UDZr8LfDv284ywfnRQymt/GAU7bxAUsqbbaKSEpUer6ttxYoJg9FQs3iaoTcTV5AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d68480fdde878b2ad1cadf82e3e6714420d4b17d379b1b66847afbde97165980","last_reissued_at":"2026-07-05T12:03:32.755073Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T12:03:32.755073Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"End to End Autoencoder MLP Framework for Sepsis Prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"Bin Yi, Di Wu, Hejiang Cai, Ji Xu, Xiang Liu, Xin Shu, Yiziting Zhu, Yujie Li","submitted_at":"2025-08-26T05:22:48Z","abstract_excerpt":"Sepsis is a life threatening condition that requires timely detection in intensive care settings. Traditional machine learning approaches, including Naive Bayes, Support Vector Machine (SVM), Random Forest, and XGBoost, often rely on manual feature engineering and struggle with irregular, incomplete time-series data commonly present in electronic health records. We introduce an end-to-end deep learning framework integrating an unsupervised autoencoder for automatic feature extraction with a multilayer perceptron classifier for binary sepsis risk prediction. To enhance clinical applicability, w"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2508.18688","kind":"arxiv","version":2},"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/2508.18688/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":"2508.18688","created_at":"2026-07-05T12:03:32.755144+00:00"},{"alias_kind":"arxiv_version","alias_value":"2508.18688v2","created_at":"2026-07-05T12:03:32.755144+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2508.18688","created_at":"2026-07-05T12:03:32.755144+00:00"},{"alias_kind":"pith_short_12","alias_value":"22CIB7O6Q6FS","created_at":"2026-07-05T12:03:32.755144+00:00"},{"alias_kind":"pith_short_16","alias_value":"22CIB7O6Q6FSVUOK","created_at":"2026-07-05T12:03:32.755144+00:00"},{"alias_kind":"pith_short_8","alias_value":"22CIB7O6","created_at":"2026-07-05T12:03:32.755144+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/22CIB7O6Q6FSVUOK36BOHZTRIQ","json":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ.json","graph_json":"https://pith.science/api/pith-number/22CIB7O6Q6FSVUOK36BOHZTRIQ/graph.json","events_json":"https://pith.science/api/pith-number/22CIB7O6Q6FSVUOK36BOHZTRIQ/events.json","paper":"https://pith.science/paper/22CIB7O6"},"agent_actions":{"view_html":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ","download_json":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ.json","view_paper":"https://pith.science/paper/22CIB7O6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2508.18688&json=true","fetch_graph":"https://pith.science/api/pith-number/22CIB7O6Q6FSVUOK36BOHZTRIQ/graph.json","fetch_events":"https://pith.science/api/pith-number/22CIB7O6Q6FSVUOK36BOHZTRIQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ/action/storage_attestation","attest_author":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ/action/author_attestation","sign_citation":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ/action/citation_signature","submit_replication":"https://pith.science/pith/22CIB7O6Q6FSVUOK36BOHZTRIQ/action/replication_record"}},"created_at":"2026-07-05T12:03:32.755144+00:00","updated_at":"2026-07-05T12:03:32.755144+00:00"}