{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2016:UAKW5THKM4Y4LRLNITN5NCRJ5A","short_pith_number":"pith:UAKW5THK","canonical_record":{"source":{"id":"1608.00647","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-02T00:09:22Z","cross_cats_sorted":[],"title_canon_sha256":"1529c3b1b40565c34da40186ceed5e15350e444dcad6605b6cd4b82f0d27c0bc","abstract_canon_sha256":"da6aea69a066936464f9f43e4302013e61212a3443b824162dff30203fcb5391"},"schema_version":"1.0"},"canonical_sha256":"a0156eccea6731c5c56d44dbd68a29e818661330a020ea31033f5173e5f001f1","source":{"kind":"arxiv","id":"1608.00647","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00647","created_at":"2026-05-18T01:04:08Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00647v3","created_at":"2026-05-18T01:04:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00647","created_at":"2026-05-18T01:04:08Z"},{"alias_kind":"pith_short_12","alias_value":"UAKW5THKM4Y4","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UAKW5THKM4Y4LRLN","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UAKW5THK","created_at":"2026-05-18T12:30:46Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2016:UAKW5THKM4Y4LRLNITN5NCRJ5A","target":"record","payload":{"canonical_record":{"source":{"id":"1608.00647","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-02T00:09:22Z","cross_cats_sorted":[],"title_canon_sha256":"1529c3b1b40565c34da40186ceed5e15350e444dcad6605b6cd4b82f0d27c0bc","abstract_canon_sha256":"da6aea69a066936464f9f43e4302013e61212a3443b824162dff30203fcb5391"},"schema_version":"1.0"},"canonical_sha256":"a0156eccea6731c5c56d44dbd68a29e818661330a020ea31033f5173e5f001f1","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:04:08.801211Z","signature_b64":"in2gejca4yh/Uo/NP2m0IeGZgnV0RElw8W8k5IeIlCttbduG4YG6Ju0gXm4PW2rriDu/Uwi0MP/r6g/u/UdFBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a0156eccea6731c5c56d44dbd68a29e818661330a020ea31033f5173e5f001f1","last_reissued_at":"2026-05-18T01:04:08.800652Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:04:08.800652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1608.00647","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-18T01:04:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"K4LpdB12AiVVriLxDsn2KEiVOrc7AwEypMNbGl5aXqS9Be+TOv2LnNTscAjdx+1ztXzEh6pjadyjwgwWZ9U+CA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T11:31:46.953417Z"},"content_sha256":"1d34893ecbc3633a5be7bf436e280dfea66ac79143933f2a37ca1ee042d11401","schema_version":"1.0","event_id":"sha256:1d34893ecbc3633a5be7bf436e280dfea66ac79143933f2a37ca1ee042d11401"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2016:UAKW5THKM4Y4LRLNITN5NCRJ5A","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.LG","authors_text":"David Sontag, Jake Marcus, Narges Razavian","submitted_at":"2016-08-02T00:09:22Z","abstract_excerpt":"Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00647","kind":"arxiv","version":3},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"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":"2026-05-18T01:04:08Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ICpFQLY/6FtwBcJfVcyv15CfaPcgJQSebDWtBXl0zAdtLbXMttVigQGuLSy4mFb5JT6PI8fzaUyog/X4qKZIBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-02T11:31:46.953770Z"},"content_sha256":"597a2c51e47bef880762318ba6cab17fb0ea3a6832e5c6d73d105f3f3f7bcbd6","schema_version":"1.0","event_id":"sha256:597a2c51e47bef880762318ba6cab17fb0ea3a6832e5c6d73d105f3f3f7bcbd6"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A/bundle.json","state_url":"https://pith.science/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A/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":"2026-06-02T11:31:46Z","links":{"resolver":"https://pith.science/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A","bundle":"https://pith.science/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A/bundle.json","state":"https://pith.science/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A/state.json","well_known_bundle":"https://pith.science/.well-known/pith/UAKW5THKM4Y4LRLNITN5NCRJ5A/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2016:UAKW5THKM4Y4LRLNITN5NCRJ5A","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":"da6aea69a066936464f9f43e4302013e61212a3443b824162dff30203fcb5391","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-02T00:09:22Z","title_canon_sha256":"1529c3b1b40565c34da40186ceed5e15350e444dcad6605b6cd4b82f0d27c0bc"},"schema_version":"1.0","source":{"id":"1608.00647","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1608.00647","created_at":"2026-05-18T01:04:08Z"},{"alias_kind":"arxiv_version","alias_value":"1608.00647v3","created_at":"2026-05-18T01:04:08Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1608.00647","created_at":"2026-05-18T01:04:08Z"},{"alias_kind":"pith_short_12","alias_value":"UAKW5THKM4Y4","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_16","alias_value":"UAKW5THKM4Y4LRLN","created_at":"2026-05-18T12:30:46Z"},{"alias_kind":"pith_short_8","alias_value":"UAKW5THK","created_at":"2026-05-18T12:30:46Z"}],"graph_snapshots":[{"event_id":"sha256:597a2c51e47bef880762318ba6cab17fb0ea3a6832e5c6d73d105f3f3f7bcbd6","target":"graph","created_at":"2026-05-18T01:04:08Z","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"},"paper":{"abstract_excerpt":"Disparate areas of machine learning have benefited from models that can take raw data with little preprocessing as input and learn rich representations of that raw data in order to perform well on a given prediction task. We evaluate this approach in healthcare by using longitudinal measurements of lab tests, one of the more raw signals of a patient's health state widely available in clinical data, to predict disease onsets. In particular, we train a Long Short-Term Memory (LSTM) recurrent neural network and two novel convolutional neural networks for multi-task prediction of disease onset for","authors_text":"David Sontag, Jake Marcus, Narges Razavian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-02T00:09:22Z","title":"Multi-task Prediction of Disease Onsets from Longitudinal Lab Tests"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1608.00647","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":"sha256:1d34893ecbc3633a5be7bf436e280dfea66ac79143933f2a37ca1ee042d11401","target":"record","created_at":"2026-05-18T01:04:08Z","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":"da6aea69a066936464f9f43e4302013e61212a3443b824162dff30203fcb5391","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2016-08-02T00:09:22Z","title_canon_sha256":"1529c3b1b40565c34da40186ceed5e15350e444dcad6605b6cd4b82f0d27c0bc"},"schema_version":"1.0","source":{"id":"1608.00647","kind":"arxiv","version":3}},"canonical_sha256":"a0156eccea6731c5c56d44dbd68a29e818661330a020ea31033f5173e5f001f1","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"a0156eccea6731c5c56d44dbd68a29e818661330a020ea31033f5173e5f001f1","first_computed_at":"2026-05-18T01:04:08.800652Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T01:04:08.800652Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"in2gejca4yh/Uo/NP2m0IeGZgnV0RElw8W8k5IeIlCttbduG4YG6Ju0gXm4PW2rriDu/Uwi0MP/r6g/u/UdFBA==","signature_status":"signed_v1","signed_at":"2026-05-18T01:04:08.801211Z","signed_message":"canonical_sha256_bytes"},"source_id":"1608.00647","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1d34893ecbc3633a5be7bf436e280dfea66ac79143933f2a37ca1ee042d11401","sha256:597a2c51e47bef880762318ba6cab17fb0ea3a6832e5c6d73d105f3f3f7bcbd6"],"state_sha256":"82cd7c79baaf78a1f30adde27658cc30952056affeb926f5c18d1e3472f3bcf7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"s7M9elkIDRkjVRHXY4d8DLtsZDLHAN9gEbm5N+k3q/VwvHC95YGwE+MtY29p9hGh5IOezb5wtnRt8ebo/KFvDQ==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-02T11:31:46.955742Z","bundle_sha256":"3eeec9e367da500fa538361fe1d0328eb560ec46e9fbce9fb352073830a23b63"}}