{"id":"https://openalex.org/W4416017711","doi":"https://doi.org/10.1145/3746252.3761381","title":"ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions","display_name":"ProtoEHR: Hierarchical Prototype Learning for EHR-based Healthcare Predictions","publication_year":2025,"publication_date":"2025-11-08","ids":{"openalex":"https://openalex.org/W4416017711","doi":"https://doi.org/10.1145/3746252.3761381"},"language":null,"primary_location":{"id":"doi:10.1145/3746252.3761381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5024571532","display_name":"Zi Cai","orcid":"https://orcid.org/0009-0001-0846-629X"},"institutions":[{"id":"https://openalex.org/I241749","display_name":"University of Cambridge","ror":"https://ror.org/013meh722","country_code":"GB","type":"education","lineage":["https://openalex.org/I241749"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zi Cai","raw_affiliation_strings":["University of Cambridge, Cambridge, United Kingdom"],"raw_orcid":"https://orcid.org/0009-0001-0846-629X","affiliations":[{"raw_affiliation_string":"University of Cambridge, Cambridge, United Kingdom","institution_ids":["https://openalex.org/I241749"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5004545610","display_name":"Yu Liu","orcid":"https://orcid.org/0000-0002-2399-2829"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Yu Liu","raw_affiliation_strings":["University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-2399-2829","affiliations":[{"raw_affiliation_string":"University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5020502914","display_name":"Zhiyao Luo","orcid":"https://orcid.org/0000-0003-0015-2023"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zhiyao Luo","raw_affiliation_strings":["University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0003-0015-2023","affiliations":[{"raw_affiliation_string":"University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055850985","display_name":"Tingting Zhu","orcid":"https://orcid.org/0000-0002-1552-5630"},"institutions":[{"id":"https://openalex.org/I40120149","display_name":"University of Oxford","ror":"https://ror.org/052gg0110","country_code":"GB","type":"education","lineage":["https://openalex.org/I40120149"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Tingting Zhu","raw_affiliation_strings":["University of Oxford, Oxford, United Kingdom"],"raw_orcid":"https://orcid.org/0000-0002-1552-5630","affiliations":[{"raw_affiliation_string":"University of Oxford, Oxford, United Kingdom","institution_ids":["https://openalex.org/I40120149"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.6397,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.86598621,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":94,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"169","last_page":"178"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9846000075340271,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11636","display_name":"Artificial Intelligence in Healthcare and Education","score":0.003599999938160181,"subfield":{"id":"https://openalex.org/subfields/2718","display_name":"Health Informatics"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.0019000000320374966,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.7386000156402588},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.5309000015258789},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.5214999914169312},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.42480000853538513},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.39579999446868896},{"id":"https://openalex.org/keywords/scheme","display_name":"Scheme (mathematics)","score":0.3889000117778778},{"id":"https://openalex.org/keywords/medical-classification","display_name":"Medical classification","score":0.3799999952316284},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.36910000443458557},{"id":"https://openalex.org/keywords/external-data-representation","display_name":"External Data Representation","score":0.3357999920845032}],"concepts":[{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.7386000156402588},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7179999947547913},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5483999848365784},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5440999865531921},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.5309000015258789},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.5214999914169312},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.42480000853538513},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.39579999446868896},{"id":"https://openalex.org/C77618280","wikidata":"https://www.wikidata.org/wiki/Q1155772","display_name":"Scheme (mathematics)","level":2,"score":0.3889000117778778},{"id":"https://openalex.org/C154874363","wikidata":"https://www.wikidata.org/wiki/Q3518464","display_name":"Medical classification","level":2,"score":0.3799999952316284},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.36910000443458557},{"id":"https://openalex.org/C116409475","wikidata":"https://www.wikidata.org/wiki/Q1385056","display_name":"External Data Representation","level":2,"score":0.3357999920845032},{"id":"https://openalex.org/C59404180","wikidata":"https://www.wikidata.org/wiki/Q17013334","display_name":"Feature learning","level":2,"score":0.3334999978542328},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.33219999074935913},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.32179999351501465},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.3199000060558319},{"id":"https://openalex.org/C72634772","wikidata":"https://www.wikidata.org/wiki/Q386824","display_name":"Data integration","level":2,"score":0.31839999556541443},{"id":"https://openalex.org/C161301231","wikidata":"https://www.wikidata.org/wiki/Q3478658","display_name":"Knowledge representation and reasoning","level":2,"score":0.3154999911785126},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.3149000108242035},{"id":"https://openalex.org/C2775955345","wikidata":"https://www.wikidata.org/wiki/Q7449071","display_name":"Semantic mapping","level":2,"score":0.3116999864578247},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3070000112056732},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.3046000003814697},{"id":"https://openalex.org/C188198153","wikidata":"https://www.wikidata.org/wiki/Q1613840","display_name":"Limiting","level":2,"score":0.2978000044822693},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.2921999990940094},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2858999967575073},{"id":"https://openalex.org/C135572916","wikidata":"https://www.wikidata.org/wiki/Q193351","display_name":"Data warehouse","level":2,"score":0.27309998869895935},{"id":"https://openalex.org/C105002631","wikidata":"https://www.wikidata.org/wiki/Q4833645","display_name":"Subject-matter expert","level":3,"score":0.27129998803138733},{"id":"https://openalex.org/C136389625","wikidata":"https://www.wikidata.org/wiki/Q334384","display_name":"Supervised learning","level":3,"score":0.26660001277923584},{"id":"https://openalex.org/C22367795","wikidata":"https://www.wikidata.org/wiki/Q7625208","display_name":"Structured prediction","level":2,"score":0.2653999924659729},{"id":"https://openalex.org/C2777220311","wikidata":"https://www.wikidata.org/wiki/Q6423340","display_name":"Knowledge acquisition","level":2,"score":0.2639000117778778},{"id":"https://openalex.org/C2988170871","wikidata":"https://www.wikidata.org/wiki/Q11000047","display_name":"Healthcare system","level":3,"score":0.2524000108242035}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3746252.3761381","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3746252.3761381","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 34th ACM International Conference on Information and Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":18,"referenced_works":["https://openalex.org/W2090443364","https://openalex.org/W2396881363","https://openalex.org/W2597505554","https://openalex.org/W2949676527","https://openalex.org/W2964068143","https://openalex.org/W2965570621","https://openalex.org/W2994785205","https://openalex.org/W2998409174","https://openalex.org/W3004001630","https://openalex.org/W3177373898","https://openalex.org/W3209819631","https://openalex.org/W4206629261","https://openalex.org/W4250042253","https://openalex.org/W4313439128","https://openalex.org/W4367046802","https://openalex.org/W4382240019","https://openalex.org/W4386076593","https://openalex.org/W4412877113"],"related_works":[],"abstract_inverted_index":{"Digital":[0],"healthcare":[1,9,13,22,67,211],"systems":[2],"have":[3],"enabled":[4],"the":[5,58,108,180,195],"collection":[6],"of":[7,33,62,80,161,164,167,169,175,182],"mass":[8],"data":[10,64],"in":[11,150,194,210],"electronic":[12],"records":[14],"(EHRs),":[15],"allowing":[16],"artificial":[17],"intelligence":[18],"solutions":[19],"for":[20],"various":[21],"prediction":[23,160,163,166,174],"tasks.":[24],"However,":[25],"existing":[26],"studies":[27],"often":[28],"focus":[29],"on":[30,112,154,202],"isolated":[31],"components":[32],"EHR":[34,63],"data,":[35],"limiting":[36],"their":[37],"predictive":[38],"performance":[39],"and":[40,75,86,101,139,173,188,205],"interpretability.":[41],"To":[42,142],"address":[43],"this":[44],"gap,":[45],"we":[46,114,147],"propose":[47],"ProtoEHR,":[48],"an":[49],"interpretable":[50,189,200],"hierarchical":[51,78,117],"prototype":[52,130],"learning":[53,119],"framework":[54,120],"that":[55,121],"fully":[56],"exploits":[57],"rich,":[59],"multi-level":[60],"structure":[61],"to":[65,94,135,184,192,208],"enhance":[66],"predictions.":[68],"More":[69],"specifically,":[70],"ProtoEHR":[71,149,183,198],"models":[72,93],"relationships":[73,97],"within":[74,132],"across":[76,125],"three":[77,126],"levels":[79,207],"EHRs:":[81],"medical":[82,99,104],"codes,":[83],"hospital":[84],"visits,":[85],"patients.":[87],"We":[88],"first":[89],"leverage":[90],"large":[91],"language":[92],"extract":[95],"semantic":[96],"among":[98],"codes":[100],"construct":[102],"a":[103,116,144],"knowledge":[105,109],"graph":[106],"as":[107],"source.":[110],"Building":[111],"this,":[113],"design":[115],"representation":[118],"captures":[122],"contextualized":[123],"representations":[124],"levels,":[127],"while":[128],"incorporating":[129],"information":[131],"each":[133],"level":[134],"capture":[136],"intrinsic":[137],"similarities":[138],"improve":[140],"generalization.":[141],"perform":[143],"comprehensive":[145],"assessment,":[146],"evaluate":[148],"two":[151],"public":[152],"datasets":[153],"five":[155],"clinically":[156],"significant":[157],"tasks,":[158],"including":[159],"mortality,":[162],"readmission,":[165],"length":[168],"stay,":[170],"drug":[171],"recommendation,":[172],"phenotype.":[176],"The":[177],"results":[178],"demonstrate":[179],"ability":[181],"make":[185],"accurate,":[186],"robust,":[187],"predictions":[190],"compared":[191],"baselines":[193],"literature.":[196],"Furthermore,":[197],"offers":[199],"insights":[201],"code,":[203],"visit,":[204],"patient":[206],"aid":[209],"prediction.":[212]},"counts_by_year":[{"year":2026,"cited_by_count":1}],"updated_date":"2026-08-05T07:39:15.569665","created_date":"2025-11-08T00:00:00"}