{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2018:M2C76OLTQMXG34L7O7VCMOVAV4","short_pith_number":"pith:M2C76OLT","canonical_record":{"source":{"id":"1806.01551","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-05T08:26:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f56124d8b9d4b27225e0599a4f3938941cbf83d983ec50ece9cda48732bbe9d3","abstract_canon_sha256":"a22f444ffa7f550ebefed3e56b08e9ae4ecb552e84c6d5c6b39168f2ec853a1a"},"schema_version":"1.0"},"canonical_sha256":"6685ff3973832e6df17f77ea263aa0af3205c480077378e4a2f15e913d991ee6","source":{"kind":"arxiv","id":"1806.01551","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.01551","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"arxiv_version","alias_value":"1806.01551v3","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.01551","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"pith_short_12","alias_value":"M2C76OLTQMXG","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"pith_short_16","alias_value":"M2C76OLTQMXG34L7","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"pith_short_8","alias_value":"M2C76OLT","created_at":"2026-07-05T00:21:31Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2018:M2C76OLTQMXG34L7O7VCMOVAV4","target":"record","payload":{"canonical_record":{"source":{"id":"1806.01551","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-05T08:26:53Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"f56124d8b9d4b27225e0599a4f3938941cbf83d983ec50ece9cda48732bbe9d3","abstract_canon_sha256":"a22f444ffa7f550ebefed3e56b08e9ae4ecb552e84c6d5c6b39168f2ec853a1a"},"schema_version":"1.0"},"canonical_sha256":"6685ff3973832e6df17f77ea263aa0af3205c480077378e4a2f15e913d991ee6","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T00:21:31.149045Z","signature_b64":"TthJ8LZPCLrXij22XEowG9N1WtZBz7PG6fRy6s11qtguxOJGxchhsiIfhFjAWoNnXnTqk+sqc6IFxGT0QvJDAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6685ff3973832e6df17f77ea263aa0af3205c480077378e4a2f15e913d991ee6","last_reissued_at":"2026-07-05T00:21:31.148561Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T00:21:31.148561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1806.01551","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":"2026-07-05T00:21:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"xLwtYaWAZyu8ZYaHb7ayYa0ZGHLffvLPO6locDYwqH4rmO7vxnK9pvOEvdf6dj20l57/V7hbS6w2lxtsMITYCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:15:29.411498Z"},"content_sha256":"b61a7ed703b49254923ab0b02393e32f53e3a016cf7152d22949a569275e75c2","schema_version":"1.0","event_id":"sha256:b61a7ed703b49254923ab0b02393e32f53e3a016cf7152d22949a569275e75c2"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2018:M2C76OLTQMXG34L7O7VCMOVAV4","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"stat.ML","authors_text":"Eunho Yang, Ingyo Chung, Juho Lee, Kwang Joon Kim, Saehoon Kim, Sung Ju Hwang","submitted_at":"2018-06-05T08:26:53Z","abstract_excerpt":"We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01551","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":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/1806.01551/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":"2026-07-05T00:21:31Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"22jKomEyQAovIgL/0MXTq8YjSpTfusutvKVIUkScnpnlFl+4ntIuZkFyW3r8pxgtU3Pt090QD1nHhfMczy4MDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-16T14:15:29.412351Z"},"content_sha256":"8e3ea4e0669142ce5b55f382f0d504efec2526377a7557b5844cab519879eb9e","schema_version":"1.0","event_id":"sha256:8e3ea4e0669142ce5b55f382f0d504efec2526377a7557b5844cab519879eb9e"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/M2C76OLTQMXG34L7O7VCMOVAV4/bundle.json","state_url":"https://pith.science/pith/M2C76OLTQMXG34L7O7VCMOVAV4/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/M2C76OLTQMXG34L7O7VCMOVAV4/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":"2026-08-16T14:15:29Z","links":{"resolver":"https://pith.science/pith/M2C76OLTQMXG34L7O7VCMOVAV4","bundle":"https://pith.science/pith/M2C76OLTQMXG34L7O7VCMOVAV4/bundle.json","state":"https://pith.science/pith/M2C76OLTQMXG34L7O7VCMOVAV4/state.json","well_known_bundle":"https://pith.science/.well-known/pith/M2C76OLTQMXG34L7O7VCMOVAV4/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2018:M2C76OLTQMXG34L7O7VCMOVAV4","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":"a22f444ffa7f550ebefed3e56b08e9ae4ecb552e84c6d5c6b39168f2ec853a1a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-05T08:26:53Z","title_canon_sha256":"f56124d8b9d4b27225e0599a4f3938941cbf83d983ec50ece9cda48732bbe9d3"},"schema_version":"1.0","source":{"id":"1806.01551","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1806.01551","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"arxiv_version","alias_value":"1806.01551v3","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1806.01551","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"pith_short_12","alias_value":"M2C76OLTQMXG","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"pith_short_16","alias_value":"M2C76OLTQMXG34L7","created_at":"2026-07-05T00:21:31Z"},{"alias_kind":"pith_short_8","alias_value":"M2C76OLT","created_at":"2026-07-05T00:21:31Z"}],"graph_snapshots":[{"event_id":"sha256:8e3ea4e0669142ce5b55f382f0d504efec2526377a7557b5844cab519879eb9e","target":"graph","created_at":"2026-07-05T00:21:31Z","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/1806.01551/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"We present a personalized and reliable prediction model for healthcare, which can provide individually tailored medical services such as diagnosis, disease treatment, and prevention. Our proposed framework targets at making personalized and reliable predictions from time-series data, such as Electronic Health Records (EHR), by modeling two complementary components: i) a shared component that captures global trend across diverse patients and ii) a patient-specific component that models idiosyncratic variability for each patient. To this end, we propose a composite model of a deep neural network","authors_text":"Eunho Yang, Ingyo Chung, Juho Lee, Kwang Joon Kim, Saehoon Kim, Sung Ju Hwang","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-05T08:26:53Z","title":"Deep Mixed Effect Model using Gaussian Processes: A Personalized and Reliable Prediction for Healthcare"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1806.01551","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:b61a7ed703b49254923ab0b02393e32f53e3a016cf7152d22949a569275e75c2","target":"record","created_at":"2026-07-05T00:21:31Z","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":"a22f444ffa7f550ebefed3e56b08e9ae4ecb552e84c6d5c6b39168f2ec853a1a","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2018-06-05T08:26:53Z","title_canon_sha256":"f56124d8b9d4b27225e0599a4f3938941cbf83d983ec50ece9cda48732bbe9d3"},"schema_version":"1.0","source":{"id":"1806.01551","kind":"arxiv","version":3}},"canonical_sha256":"6685ff3973832e6df17f77ea263aa0af3205c480077378e4a2f15e913d991ee6","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"6685ff3973832e6df17f77ea263aa0af3205c480077378e4a2f15e913d991ee6","first_computed_at":"2026-07-05T00:21:31.148561Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T00:21:31.148561Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"TthJ8LZPCLrXij22XEowG9N1WtZBz7PG6fRy6s11qtguxOJGxchhsiIfhFjAWoNnXnTqk+sqc6IFxGT0QvJDAQ==","signature_status":"signed_v1","signed_at":"2026-07-05T00:21:31.149045Z","signed_message":"canonical_sha256_bytes"},"source_id":"1806.01551","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:b61a7ed703b49254923ab0b02393e32f53e3a016cf7152d22949a569275e75c2","sha256:8e3ea4e0669142ce5b55f382f0d504efec2526377a7557b5844cab519879eb9e"],"state_sha256":"99691ca1ca0e23b9a0a64a508bf0ab11ab1cf38949903d409239959f0185f565"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"940zH69GECcAM7RMI/0GaQbIwnE3LKSH/Gwr0J4yqNNNbT+Edf0+CUZmy8ED59D3PAHb/SIsvg36goWzZtmoDw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-16T14:15:29.419583Z","bundle_sha256":"0b96fddbd748724521fdc759df25e30ad9db8f6b7b7c2947b4f0ebeaa5bc8859"}}