{"id":"https://openalex.org/W3209819631","doi":"https://doi.org/10.1145/3459637.3482273","title":"MedRetriever","display_name":"MedRetriever","publication_year":2021,"publication_date":"2021-10-26","ids":{"openalex":"https://openalex.org/W3209819631","doi":"https://doi.org/10.1145/3459637.3482273","mag":"3209819631"},"language":"en","primary_location":{"id":"doi:10.1145/3459637.3482273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; 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/A5024079930","display_name":"Muchao Ye","orcid":"https://orcid.org/0009-0006-9112-8895"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Muchao Ye","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5044416074","display_name":"Suhan Cui","orcid":"https://orcid.org/0009-0005-3932-6993"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Suhan Cui","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101752145","display_name":"Yaqing Wang","orcid":"https://orcid.org/0000-0002-1548-0727"},"institutions":[{"id":"https://openalex.org/I219193219","display_name":"Purdue University West Lafayette","ror":"https://ror.org/02dqehb95","country_code":"US","type":"education","lineage":["https://openalex.org/I219193219"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Yaqing Wang","raw_affiliation_strings":["Purdue University, West Lafayette, IN, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Purdue University, West Lafayette, IN, USA","institution_ids":["https://openalex.org/I219193219"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101912906","display_name":"Junyu Luo","orcid":"https://orcid.org/0009-0001-6894-1144"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Junyu Luo","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100645991","display_name":"Cao Xiao","orcid":"https://orcid.org/0000-0002-3869-6942"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cao Xiao","raw_affiliation_strings":["Amplitude, Seattle, WA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Amplitude, Seattle, WA, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5001030192","display_name":"Fenglong Ma","orcid":"https://orcid.org/0000-0002-4999-0303"},"institutions":[{"id":"https://openalex.org/I130769515","display_name":"Pennsylvania State University","ror":"https://ror.org/04p491231","country_code":"US","type":"education","lineage":["https://openalex.org/I130769515"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Fenglong Ma","raw_affiliation_strings":["The Pennsylvania State University, University Park, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The Pennsylvania State University, University Park, PA, USA","institution_ids":["https://openalex.org/I130769515"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":23,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"2414","last_page":"2423"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"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":0.9998999834060669,"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/T10028","display_name":"Topic Modeling","score":0.9980999827384949,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9836000204086304,"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/computer-science","display_name":"Computer science","score":0.8212020397186279},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5904074907302856},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.5793260335922241},{"id":"https://openalex.org/keywords/interpretation","display_name":"Interpretation (philosophy)","score":0.5505526065826416},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5328648090362549},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.527349591255188},{"id":"https://openalex.org/keywords/reliability","display_name":"Reliability (semiconductor)","score":0.4978907108306885},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.47072139382362366},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4525909721851349},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.43798452615737915},{"id":"https://openalex.org/keywords/electronic-health-record","display_name":"Electronic health record","score":0.43169838190078735},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3342776298522949},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.1952683925628662}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8212020397186279},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5904074907302856},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.5793260335922241},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.5505526065826416},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5328648090362549},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.527349591255188},{"id":"https://openalex.org/C43214815","wikidata":"https://www.wikidata.org/wiki/Q7310987","display_name":"Reliability (semiconductor)","level":3,"score":0.4978907108306885},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.47072139382362366},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4525909721851349},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.43798452615737915},{"id":"https://openalex.org/C3020144179","wikidata":"https://www.wikidata.org/wiki/Q10871684","display_name":"Electronic health record","level":3,"score":0.43169838190078735},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3342776298522949},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.1952683925628662},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C163258240","wikidata":"https://www.wikidata.org/wiki/Q25342","display_name":"Power (physics)","level":2,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459637.3482273","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459637.3482273","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 30th ACM International Conference on Information &amp; Knowledge Management","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Peace, Justice and strong institutions","id":"https://metadata.un.org/sdg/16","score":0.7599999904632568}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":26,"referenced_works":["https://openalex.org/W1538085078","https://openalex.org/W1647671624","https://openalex.org/W1924770834","https://openalex.org/W2064675550","https://openalex.org/W2143448637","https://openalex.org/W2404369708","https://openalex.org/W2557074642","https://openalex.org/W2690721124","https://openalex.org/W2742491462","https://openalex.org/W2767786571","https://openalex.org/W2809396336","https://openalex.org/W2809398771","https://openalex.org/W2896538705","https://openalex.org/W2914241418","https://openalex.org/W2963208729","https://openalex.org/W2963271116","https://openalex.org/W2963403868","https://openalex.org/W2963532813","https://openalex.org/W2963806310","https://openalex.org/W2964068143","https://openalex.org/W2971629451","https://openalex.org/W3003504112","https://openalex.org/W3080098168","https://openalex.org/W3093599560","https://openalex.org/W3099136959","https://openalex.org/W4214671568"],"related_works":["https://openalex.org/W187932805","https://openalex.org/W1641026212","https://openalex.org/W4312053962","https://openalex.org/W2078646730","https://openalex.org/W2087134418","https://openalex.org/W2323588885","https://openalex.org/W3047677938","https://openalex.org/W2911135505","https://openalex.org/W2920854314","https://openalex.org/W4302340031"],"abstract_inverted_index":{"The":[0],"broad":[1],"adoption":[2],"of":[3,12,20,35,57,177],"electronic":[4],"health":[5,21,108],"record":[6],"(EHR)":[7],"systems":[8],"and":[9,31,94,175,213,221,243,246],"the":[10,18,29,47,63,122,133,143,147,164,172,195,203,209,214,238,248,268],"advances":[11],"deep":[13,36],"learning":[14],"technology":[15],"have":[16],"motivated":[17],"development":[19],"risk":[22,109],"prediction":[23,42,48,68,110,220],"models,":[24],"which":[25,77,128,199,235],"mainly":[26],"depend":[27],"on":[28,257],"expressiveness":[30],"temporal":[32],"modeling":[33],"capacity":[34],"neural":[37],"networks":[38],"(DNNs)":[39],"to":[40,106,114,135,146,162,188,266],"improve":[41],"performance.":[43],"Some":[44],"further":[45],"augment":[46,107],"by":[49,70,186,251,271],"using":[50],"external":[51],"knowledge,":[52],"however,":[53],"a":[54,138,184],"great":[55],"deal":[56],"EHR":[58,165,173,211,233],"information":[59],"inevitably":[60],"loses":[61],"during":[62],"knowledge":[64],"mapping.":[65],"In":[66],"addition,":[67],"made":[69],"existing":[71],"models":[72,161],"usually":[73],"lacks":[74],"reliable":[75],"interpretation,":[76],"undermines":[78],"their":[79],"reliability":[80],"in":[81,137,202,241,255],"guiding":[82],"clinical":[83],"decision-making.":[84],"To":[85,150],"solve":[86],"these":[87],"challenges,":[88],"we":[89,262],"propose":[90],"MedRetriever,":[91],"an":[92],"effective":[93],"flexible":[95],"framework":[96],"that":[97],"leverages":[98],"unstructured":[99],"medical":[100,196],"text":[101,192,197,206,215],"collected":[102],"from":[103,142,158,194],"authoritative":[104],"websites":[105],"as":[111,113],"well":[112],"provide":[115,129],"understandable":[116],"interpretation.":[117,222],"Besides,":[118],"MedRetriever":[119,152,225],"explicitly":[120],"takes":[121],"target":[123,144,178],"disease":[124,145,179],"documents":[125,180],"into":[126,183],"consideration,":[127],"key":[130],"guidance":[131],"for":[132,167,219],"model":[134],"learn":[136,163],"target-driven":[139],"direction,":[140],"i.e.,":[141],"input":[148],"EHR.":[149],"specify,":[151],"can":[153],"flexibly":[154],"choose":[155],"its":[156],"backbone":[157],"major":[159],"predictive":[160],"embedding":[166,174,212],"each":[168],"visit.":[169],"After":[170],"that,":[171],"features":[176],"are":[181,217],"aggregated":[182],"query":[185],"self-attention":[187],"retrieve":[189],"highly":[190],"relevant":[191],"segments":[193],"pool,":[198],"is":[200],"stored":[201],"dynamically":[204],"updated":[205],"memory.":[207],"Finally,":[208],"comprehensive":[210],"memory":[216],"used":[218],"We":[223],"evaluate":[224],"against":[226],"nine":[227],"state-of-the-art":[228],"approaches":[229],"across":[230],"three":[231,258],"real-world":[232],"datasets,":[234],"consistently":[236],"achieves":[237],"best":[239,249],"performance":[240],"AUC":[242],"recall":[244,256],"metrics":[245],"outperforms":[247],"baseline":[250],"at":[252],"least":[253],"4.8%":[254],"test":[259],"datasets.":[260],"Furthermore,":[261],"conduct":[263],"case":[264],"studies":[265],"show":[267],"easy-to-understand":[269],"interpretation":[270],"MedRetriever.":[272]},"counts_by_year":[{"year":2026,"cited_by_count":6},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":5},{"year":2022,"cited_by_count":4}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2021-11-08T00:00:00"}
