{"as_of":"2026-08-15T20:26:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:1609e41b8ec95384ffe19cde202cd6339690d9df256e8736296d0290b04094f1","coverage":[{"denominator":31,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":31,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T14:01:41.238449Z","state":"measured"},{"denominator":31,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":31,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-15T06:32:42.880941+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.03971/citation-record","integrity":"/paper/1908.03971/integrity","json":"/paper/1908.03971/citation-record.json","paper":"/paper/1908.03971"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:43.274702Z","title":"Validation of the mortality prediction model for icu patients","venue":null,"work_id":"45766f68-82cd-4679-adf7-b2b350ae3616","year":1987},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.731353Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:10cdb2b112d62c7694091c1e0150526c47ef4d3be9aa911c97516a76db72a81d","observation_id":"d34a5944-2a31-4bbd-ba27-4917a4d1893f","resolution":{"observed_at":"2026-08-14T14:01:43.281372Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:43.205350Z","title":"Predicting death and readmission after intensive care discharge,","venue":null,"work_id":"8c0ba554-05f7-4ef5-b7fa-6c40ed7e770c","year":2008},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.744542Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:5471a8bdaf072615d3612fc7557f5e2d98a33b2cc440df6f2498ff318d11e80c","observation_id":"ada960f2-3881-4961-b4ea-9d9014221c92","resolution":{"observed_at":"2026-08-14T14:01:43.222658Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:40.754716Z","title":"Representation learning: A review and new perspectives,","venue":null,"work_id":null,"year":2013},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.754716Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:82cd7e2fe1c9b165fdfebc703b1cd1cab2679914af7611f9ce4b440f481e1d56","observation_id":"874646fc-c559-4983-8c14-93df788a05ce","resolution":{"observed_at":"2026-08-14T14:01:40.754716Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:43.127467Z","title":"Distributed representations of words and phrases and their compositionality,","venue":null,"work_id":"a741a7ab-e5a7-49d4-bbcf-d2bfe57e98cf","year":2013},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.767060Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:39910cf91dbcde3103f049dbbc5ea57e4bd03e1675340f360300d5b97b9a48fd","observation_id":"ea40128f-aed5-4e3b-86c2-47eb58240ec5","resolution":{"observed_at":"2026-08-14T14:01:43.151616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1806.02873","last_updated":"2018-06-06T07:45:06Z","snapshot_observed_at":"2026-08-14T19:07:01.575853Z","submitted_at":"2018-06-06T07:45:06Z","title":"Medical Concept Embedding with Time-Aware Attention","version":1},"cited_work":{"arxiv_id":"1806.02873","doi":null,"metadata_source":"pith","pith_arxiv_id":"1806.02873","snapshot_observed_at":"2026-08-14T14:01:42.423632Z","title":"Medical Concept Embedding with Time-Aware Attention","venue":"cs.CL","work_id":"e4649ccf-b31e-46d8-9c8a-3e828782f86d","year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.794231Z"},"links":{"cited_paper":"/paper/1806.02873","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:0919a443ada437e10cffb6cfbcc95aad6d4bc06807c183185ea95c31f14a2973","observation_id":"c4df3dfe-d216-48a9-a73a-47ecbf887bcd","resolution":{"observed_at":"2026-08-14T14:01:42.452219Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1409.0473","last_updated":"2016-05-19T21:53:22Z","snapshot_observed_at":"2026-08-12T12:07:33.202888Z","submitted_at":"2014-09-01T16:33:02Z","title":"Neural Machine Translation by Jointly Learning to Align and Translate","version":7},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1409.0473","snapshot_observed_at":"2026-08-14T14:01:40.810733Z","title":"Neural machine translation by jointly learning to align and translate,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.810733Z"},"links":{"cited_paper":"/paper/1409.0473","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:2366d8fba4e375004b3fd3a7384899cac686fa6bc1d273982a2b77fbc889144c","observation_id":"9a78417e-987e-4be3-a853-a86cff3f30dd","resolution":{"observed_at":"2026-08-14T14:01:40.810733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1508.04025","last_updated":"2015-09-20T08:25:52Z","snapshot_observed_at":"2026-08-14T22:36:17.759859Z","submitted_at":"2015-08-17T13:43:19Z","title":"Effective Approaches to Attention-based Neural Machine Translation","version":5},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1508.04025","snapshot_observed_at":"2026-08-14T14:01:40.822690Z","title":"Effective ap- proaches to attention-based neural machine translation,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.822690Z"},"links":{"cited_paper":"/paper/1508.04025","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:4590d5d51a94b74a87b98519ea2f107cad2cdbb8e07c826fb800932bf2746602","observation_id":"09792ba7-5e2f-44bd-91a2-2298b1346841","resolution":{"observed_at":"2026-08-14T14:01:40.822690Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:40.838721Z","title":"Attention is all you need,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.838721Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:af851a68e62c81e4f38a689258039a6c415d616eeb426dafd7088a236cc0bc22","observation_id":"7d46e582-ac1c-4316-8faf-98dcda6d2b49","resolution":{"observed_at":"2026-08-14T14:01:40.838721Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:43.002765Z","title":"Learning representations by back-propagating errors,","venue":null,"work_id":"a5493ef6-b977-4dc7-ab6a-eb2442143aef","year":1988},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.854967Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:eb2892b4053ec4be2b25c622378e4f81765387914d7746a05c8e2e091e688b42","observation_id":"e83f9562-59d8-411a-a06d-cdeda01a59b1","resolution":{"observed_at":"2026-08-14T14:01:43.024664Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.976583Z","title":null,"venue":null,"work_id":"e15e4774-a8d5-498d-bdfb-0ac19ccc5662","year":null},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.865072Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:3a298c581c19001c8f0ddda7865609b9cb43de00a6df059cd8cdec1b43010a06","observation_id":"1480f377-e841-478a-b632-e7147cd2e04e","resolution":{"observed_at":"2026-08-14T14:01:42.982934Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:40.879733Z","title":"A neural proba- bilistic language model,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.879733Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:8f7bb8d9426b95176b755e91b6a65dadab1ce3f8df6fe2469d80600ff9df26c9","observation_id":"b0476379-3ca7-41d1-9a1e-c8ec896d75f1","resolution":{"observed_at":"2026-08-14T14:01:40.879733Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.860444Z","title":"Multi-layer representation learning for med- ical concepts,","venue":null,"work_id":"b72521a2-26fd-4f69-a541-6f5421532e15","year":2016},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.902752Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:3c62b470fffda60f094906b1e59d987e7c7d64a4aaaa1afd8d12d84d99466108","observation_id":"54db43d7-c686-4881-a425-837d34a8695c","resolution":{"observed_at":"2026-08-14T14:01:42.871121Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.796582Z","title":"Deepr: a convolutional net for medical records,","venue":null,"work_id":"4f3d2459-18f5-4e0c-a985-8e4d95a0250a","year":2016},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.918764Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:acbe0711410e49de057d3edf6ec4eb5cf21760507bb9c3718e45ee08556b831e","observation_id":"57ce0bab-1d9c-4e86-b1b4-7b1a4315904d","resolution":{"observed_at":"2026-08-14T14:01:42.811314Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:40.932336Z","title":"Gram: Graph-based attention model for healthcare representation learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.932336Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:9d33bcd16b8cc9530a48b9b424b9071f6a9ebf2e9f2ca133349deb87eff0d010","observation_id":"acdefc9f-daaf-4ae4-9f3f-482211dbf98d","resolution":{"observed_at":"2026-08-14T14:01:40.932336Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:40.941250Z","title":"Patient2vec: A personalized interpretable deep representation of the longitudinal electronic health record,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.941250Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:9534f6560713b595bcbf1a664797c9115ff7734a56dbbd3451ea6c9a55a255ed","observation_id":"9252389d-6554-4290-a2ab-b7dbd025ffc7","resolution":{"observed_at":"2026-08-14T14:01:40.941250Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.731650Z","title":"Mime: Multilevel medical embedding of electronic health records for predictive healthcare,","venue":null,"work_id":"f222dd8c-7082-4a10-9d5e-ea55ef289fd0","year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.951530Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:0551b9fc360a9d4fd690dbe9070de692d7eba450f2d963ce0ca5f97aa78f6a57","observation_id":"a7ff27e5-a7dd-44d8-967f-67a65aab66ec","resolution":{"observed_at":"2026-08-14T14:01:42.743697Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.684152Z","title":"Investigating the challenges of temporal relation extraction from clinical text,","venue":null,"work_id":"bc21d10e-a1a2-44bf-984b-16454c88fbe4","year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.960182Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:5fd3edf220bc20a68620bb24a93b12acaad02433bd156e7283229a2a0fdb2a51","observation_id":"f4b18409-e3a5-4b5a-996c-b6e718d9ffeb","resolution":{"observed_at":"2026-08-14T14:01:42.691478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1805.02096","last_updated":"2018-05-05T18:20:48Z","snapshot_observed_at":"2026-08-14T19:18:28.280082Z","submitted_at":"2018-05-05T18:20:48Z","title":"Learning Patient Representations from Text","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1805.02096","snapshot_observed_at":"2026-08-14T14:01:40.971178Z","title":"Learning patient representations from text,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.971178Z"},"links":{"cited_paper":"/paper/1805.02096","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:a77c9519278f94e354fdd2085b630e2ef3280bb5b87d848fcdb9762dbf8fd6f0","observation_id":"8ffd6e41-36e8-452f-afd7-3f73ac6ab701","resolution":{"observed_at":"2026-08-14T14:01:40.971178Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1808.04928","last_updated":"2018-08-15T00:10:55Z","snapshot_observed_at":"2026-08-14T18:41:04.032560Z","submitted_at":"2018-08-15T00:10:55Z","title":"Deep EHR: Chronic Disease Prediction Using Medical Notes","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1808.04928","snapshot_observed_at":"2026-08-14T14:01:40.981465Z","title":"Deep ehr: Chronic disease prediction using medical notes,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:40.981465Z"},"links":{"cited_paper":"/paper/1808.04928","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:07afbeb8ecb559522199060230dcf350e3666f6c63c99440b4a70c3b75967af8","observation_id":"b9527ac5-9132-4c6b-9c4f-94c89b86f5c0","resolution":{"observed_at":"2026-08-14T14:01:40.981465Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.05342","last_updated":"2020-11-29T03:40:45Z","snapshot_observed_at":"2026-08-13T10:42:58.617730Z","submitted_at":"2019-04-10T17:53:13Z","title":"ClinicalBERT: Modeling Clinical Notes and Predicting Hospital Readmission","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.05342","snapshot_observed_at":"2026-08-14T14:01:41.013247Z","title":"Clinicalbert: Modeling clinical notes and predicting hospital readmission,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.013247Z"},"links":{"cited_paper":"/paper/1904.05342","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:83e610bacc372bebc29e66ecf303abcf258fdf772cc3caaf7f38c4f1ed2bc10a","observation_id":"c3a806d8-47fb-4a1e-95a7-5bcb86202d15","resolution":{"observed_at":"2026-08-14T14:01:41.013247Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.652388Z","title":"Ehr phenotyping via jointly embedding medical concepts and words into a unified vector space,","venue":null,"work_id":"779a97c3-17e3-47fa-84ce-c27423894b01","year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.065711Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:28afc18eb3be5b2bd3d19603767f67b9bdb0b1d8efd9e2ab6c336ecde716dad2","observation_id":"48932921-c8f6-4402-80f4-48a8bf5ad476","resolution":{"observed_at":"2026-08-14T14:01:42.659160Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1802.05695","last_updated":"2018-04-16T21:45:35Z","snapshot_observed_at":"2026-08-14T19:45:27.324318Z","submitted_at":"2018-02-15T18:25:32Z","title":"Explainable Prediction of Medical Codes from Clinical Text","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1802.05695","snapshot_observed_at":"2026-08-14T14:01:41.083579Z","title":"Ex- plainable prediction of medical codes from clinical text,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.083579Z"},"links":{"cited_paper":"/paper/1802.05695","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:c357c92e70cf6c42d49b6c66ff1c3dc3a9a05a6a2b4269389e41d6e3b2fd8cea","observation_id":"da132517-7ad0-42c3-a1cb-79ddada1ce8c","resolution":{"observed_at":"2026-08-14T14:01:41.083579Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1810.04805","last_updated":"2019-05-24T20:37:26Z","snapshot_observed_at":"2026-08-14T18:16:28.847993Z","submitted_at":"2018-10-11T00:50:01Z","title":"BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1810.04805","snapshot_observed_at":"2026-08-14T14:01:41.108563Z","title":"BERT: pre-training of deep bidirectional transformers for language understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.108563Z"},"links":{"cited_paper":"/paper/1810.04805","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:51d194edf1e44ce15b2bd9022de0fa05da23b336f771e8743db4bc8286dfbe32","observation_id":"8d1cf2b2-6566-47dd-838d-6c6094769a48","resolution":{"observed_at":"2026-08-14T14:01:41.108563Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:41.119974Z","title":"Extract- ing and composing robust features with denoising autoencoders,","venue":null,"work_id":null,"year":2008},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.119974Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:eee055eb8f47a4fcba837673e7add9201450f59c7df080d899d1f65230bdd222","observation_id":"55ea53ab-c9ba-4ca7-b666-302da515c0d4","resolution":{"observed_at":"2026-08-14T14:01:41.119974Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1811.01249","last_updated":"2018-11-03T17:14:28Z","snapshot_observed_at":"2026-08-14T18:04:44.214752Z","submitted_at":"2018-11-03T17:14:28Z","title":"Dynamic Feature Acquisition Using Denoising Autoencoders","version":1},"cited_work":{"arxiv_id":"1811.01249","doi":null,"metadata_source":"pith","pith_arxiv_id":"1811.01249","snapshot_observed_at":"2026-08-14T14:01:41.572297Z","title":"Dynamic Feature Acquisition Using Denoising Autoencoders","venue":"cs.LG","work_id":"573fd07a-bef0-4211-9389-6082ebd1c628","year":2018},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.137535Z"},"links":{"cited_paper":"/paper/1811.01249","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:3757634fbc70d5dd0ebb572f7edee8732f9c885ff5241dd34788d6150c2d0a7a","observation_id":"61771e81-2c0b-47ab-8e8e-3ee9127ab9e6","resolution":{"observed_at":"2026-08-14T14:01:41.590193Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1901.08746","last_updated":"2019-10-18T02:51:31Z","snapshot_observed_at":"2026-08-14T17:26:06.374965Z","submitted_at":"2019-01-25T05:57:24Z","title":"BioBERT: a pre-trained biomedical language representation model for biomedical text mining","version":4},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1901.08746","snapshot_observed_at":"2026-08-14T14:01:41.179160Z","title":"Biobert: pre-trained biomedical language representation model for biomedical text mining,","venue":null,"work_id":null,"year":1901},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.179160Z"},"links":{"cited_paper":"/paper/1901.08746","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:3af7286973d144c94c9e2c850d99607f9005032e0591133133178728f32aec30","observation_id":"b5d89a94-d287-4bc5-97ef-2e1a5eaf0754","resolution":{"observed_at":"2026-08-14T14:01:41.179160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.582508Z","title":"Mimic-iii, a freely accessible critical care database,","venue":null,"work_id":"0ccc37a1-0df6-4acc-ae3f-a9e024180d3c","year":2016},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.189354Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:ca1f1826060246a2ff3f44f12930745dabf8d350258fe594092f2a74ef8bd21e","observation_id":"6c50a06e-832e-47f0-b3ad-f83c6e053fd5","resolution":{"observed_at":"2026-08-14T14:01:42.601606Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-08-14T18:51:16.666127Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-14T14:01:41.207160Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.207160Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:26e84390c27c0d2e9516426a9571c3fb432a5adb2c26167fd2d51b537e228f6f","observation_id":"1a765a8e-8998-4e84-a592-f12473cb3e58","resolution":{"observed_at":"2026-08-14T14:01:41.207160Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1904.03323","last_updated":"2019-06-20T20:41:58Z","snapshot_observed_at":"2026-08-14T16:50:51.644744Z","submitted_at":"2019-04-06T00:34:39Z","title":"Publicly Available Clinical BERT Embeddings","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1904.03323","snapshot_observed_at":"2026-08-14T14:01:41.219889Z","title":"Publicly available clinical bert embeddings,","venue":null,"work_id":null,"year":1904},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.219889Z"},"links":{"cited_paper":"/paper/1904.03323","citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:e389551fb87a852923dec054e038600a811e1257b3032f23194fbe6e38cf758b","observation_id":"3d3f6a3b-aaca-45c5-8f27-904738d1fd9b","resolution":{"observed_at":"2026-08-14T14:01:41.219889Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:42.512739Z","title":"Professor forcing: A new algorithm for training recurrent networks,","venue":null,"work_id":"4047cbb4-b8c7-47cb-9247-1d44fd0ef142","year":2016},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.227798Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:886b4f565360f69e8e6a008f24525f7c08564b98f51103bb71d759f8d89ffea5","observation_id":"b176784d-8a8b-4682-96de-f34bc0503fc4","resolution":{"observed_at":"2026-08-14T14:01:42.543297Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-15T06:32:42.880941+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T14:01:41.238449Z","title":"Automatic differentiation in PyTorch,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation","version":4},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T14:01:41.238449Z"},"links":{"citing_paper":"/paper/1908.03971"},"observation_digest":"sha256:5c037efe825101009da5c5d78c0b5734df6ed0c9a6e9f7c1ed588efe5732130b","observation_id":"b599f8dc-4684-4450-a966-e3954ae5b807","resolution":{"observed_at":"2026-08-14T14:01:41.238449Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"1908.03971","last_updated":"2020-05-03T10:32:10Z","latest_version":4,"primary_category":"cs.LG","snapshot_observed_at":"2026-08-15T16:22:57.414738Z","submitted_at":"2019-08-11T23:15:23Z","title":"TAPER: Time-Aware Patient EHR Representation"},"reference_resolution":{"displayed":31,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":2,"verified_fuzzy":11},"total_outbound_references":31},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-15T06:32:42.880941+00:00","source":"crossref"},{"observed_at":"2026-08-15T06:32:39.529945+00:00","source":"retraction_watch"}],"thesis":"As of 15 August 2026, this Paper Citation Record lists 31 of 31 outbound references and 0 inbound Pith citation observations for arXiv:1908.03971."}