{"id":"https://openalex.org/W2913198899","doi":"https://doi.org/10.1145/3308558.3313485","title":"Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources","display_name":"Improving Medical Code Prediction from Clinical Text via Incorporating Online Knowledge Sources","publication_year":2019,"publication_date":"2019-05-13","ids":{"openalex":"https://openalex.org/W2913198899","doi":"https://doi.org/10.1145/3308558.3313485","mag":"2913198899"},"language":"en","primary_location":{"id":"doi:10.1145/3308558.3313485","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313485","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.1145/3308558.3313485","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5063506109","display_name":"Tian Bai","orcid":"https://orcid.org/0000-0001-9482-5553"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tian Bai","raw_affiliation_strings":["Temple University, USA"],"affiliations":[{"raw_affiliation_string":"Temple University, USA","institution_ids":["https://openalex.org/I84392919"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5059847153","display_name":"Slobodan Vu\u010deti\u0107","orcid":"https://orcid.org/0000-0001-5884-6293"},"institutions":[{"id":"https://openalex.org/I84392919","display_name":"Temple University","ror":"https://ror.org/00kx1jb78","country_code":"US","type":"education","lineage":["https://openalex.org/I84392919"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Slobodan Vucetic","raw_affiliation_strings":["Temple University, USA"],"affiliations":[{"raw_affiliation_string":"Temple University, USA","institution_ids":["https://openalex.org/I84392919"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063506109"],"corresponding_institution_ids":["https://openalex.org/I84392919"],"apc_list":null,"apc_paid":null,"fwci":3.8964,"has_fulltext":false,"cited_by_count":48,"citation_normalized_percentile":{"value":0.94820134,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"72","last_page":"82"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9993000030517578,"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/T10028","display_name":"Topic Modeling","score":0.9993000030517578,"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/T10181","display_name":"Natural Language Processing Techniques","score":0.9955999851226807,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9923999905586243,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7577420473098755},{"id":"https://openalex.org/keywords/baseline","display_name":"Baseline (sea)","score":0.7457531094551086},{"id":"https://openalex.org/keywords/exploit","display_name":"Exploit","score":0.6420518755912781},{"id":"https://openalex.org/keywords/source-code","display_name":"Source code","score":0.5931941866874695},{"id":"https://openalex.org/keywords/informatics","display_name":"Informatics","score":0.5822428464889526},{"id":"https://openalex.org/keywords/diagnosis-code","display_name":"Diagnosis code","score":0.572421669960022},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5609458684921265},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5374152660369873},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.5362960696220398},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5346727967262268},{"id":"https://openalex.org/keywords/health-informatics","display_name":"Health informatics","score":0.4852311611175537},{"id":"https://openalex.org/keywords/matching","display_name":"Matching (statistics)","score":0.46842849254608154},{"id":"https://openalex.org/keywords/set","display_name":"Set (abstract data type)","score":0.455203115940094},{"id":"https://openalex.org/keywords/emergency-department","display_name":"Emergency department","score":0.43471086025238037},{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.4200630784034729},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.3689917325973511},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.33085301518440247},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.17993226647377014},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14325487613677979},{"id":"https://openalex.org/keywords/programming-language","display_name":"Programming language","score":0.11676505208015442},{"id":"https://openalex.org/keywords/computer-security","display_name":"Computer security","score":0.11410805583000183}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7577420473098755},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.7457531094551086},{"id":"https://openalex.org/C165696696","wikidata":"https://www.wikidata.org/wiki/Q11287","display_name":"Exploit","level":2,"score":0.6420518755912781},{"id":"https://openalex.org/C43126263","wikidata":"https://www.wikidata.org/wiki/Q128751","display_name":"Source code","level":2,"score":0.5931941866874695},{"id":"https://openalex.org/C191630685","wikidata":"https://www.wikidata.org/wiki/Q4027615","display_name":"Informatics","level":2,"score":0.5822428464889526},{"id":"https://openalex.org/C45827449","wikidata":"https://www.wikidata.org/wiki/Q5270338","display_name":"Diagnosis code","level":3,"score":0.572421669960022},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5609458684921265},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5374152660369873},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.5362960696220398},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5346727967262268},{"id":"https://openalex.org/C145642194","wikidata":"https://www.wikidata.org/wiki/Q870895","display_name":"Health informatics","level":3,"score":0.4852311611175537},{"id":"https://openalex.org/C165064840","wikidata":"https://www.wikidata.org/wiki/Q1321061","display_name":"Matching (statistics)","level":2,"score":0.46842849254608154},{"id":"https://openalex.org/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"score":0.455203115940094},{"id":"https://openalex.org/C2780724011","wikidata":"https://www.wikidata.org/wiki/Q1295316","display_name":"Emergency department","level":2,"score":0.43471086025238037},{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.4200630784034729},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.3689917325973511},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.33085301518440247},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.17993226647377014},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14325487613677979},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.11676505208015442},{"id":"https://openalex.org/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.11410805583000183},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"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/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.0},{"id":"https://openalex.org/C119599485","wikidata":"https://www.wikidata.org/wiki/Q43035","display_name":"Electrical engineering","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/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"score":0.0},{"id":"https://openalex.org/C111368507","wikidata":"https://www.wikidata.org/wiki/Q43518","display_name":"Oceanography","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3308558.3313485","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313485","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.1145/3308558.3313485","is_oa":true,"landing_page_url":"https://doi.org/10.1145/3308558.3313485","pdf_url":null,"source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"The World Wide Web Conference","raw_type":"proceedings-article"},"sustainable_development_goals":[{"score":0.4699999988079071,"id":"https://metadata.un.org/sdg/4","display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":47,"referenced_works":["https://openalex.org/W658020064","https://openalex.org/W1832693441","https://openalex.org/W1880262756","https://openalex.org/W1902237438","https://openalex.org/W1934443959","https://openalex.org/W1974957625","https://openalex.org/W2007666495","https://openalex.org/W2064675550","https://openalex.org/W2078346178","https://openalex.org/W2086294181","https://openalex.org/W2096664202","https://openalex.org/W2102150019","https://openalex.org/W2131744502","https://openalex.org/W2144935315","https://openalex.org/W2147152072","https://openalex.org/W2239135493","https://openalex.org/W2313284959","https://openalex.org/W2396881363","https://openalex.org/W2407534863","https://openalex.org/W2470673105","https://openalex.org/W2517259736","https://openalex.org/W2556873163","https://openalex.org/W2576012086","https://openalex.org/W2617980815","https://openalex.org/W2626778328","https://openalex.org/W2690721124","https://openalex.org/W2769265955","https://openalex.org/W2773464447","https://openalex.org/W2798566347","https://openalex.org/W2809396336","https://openalex.org/W2903877319","https://openalex.org/W2950133940","https://openalex.org/W2951125449","https://openalex.org/W2963271116","https://openalex.org/W2963912736","https://openalex.org/W2964142373","https://openalex.org/W2964291773","https://openalex.org/W2964308564","https://openalex.org/W2985962305","https://openalex.org/W3098949126","https://openalex.org/W3099136959","https://openalex.org/W4231510805","https://openalex.org/W4238365252","https://openalex.org/W4298902798","https://openalex.org/W4385245566","https://openalex.org/W6729835178","https://openalex.org/W6739901393"],"related_works":["https://openalex.org/W2397677514","https://openalex.org/W1495820741","https://openalex.org/W2914293784","https://openalex.org/W4323056170","https://openalex.org/W2138729887","https://openalex.org/W2401910071","https://openalex.org/W2126732312","https://openalex.org/W4302329558","https://openalex.org/W1971571384","https://openalex.org/W2229314870"],"abstract_inverted_index":{"Clinical":[0],"notes":[1,29,191],"contain":[2,86],"detailed":[3,87],"information":[4,88,270],"about":[5,89,262],"health":[6,17],"status":[7],"of":[8,12,59,72,80,93,139,148,170,182,247,255],"patients":[9],"for":[10,271],"each":[11],"their":[13,96],"encounters":[14],"with":[15,168,179],"a":[16,32,38,52,62,67,126,156],"system.":[18],"Developing":[19],"effective":[20],"models":[21,219,238],"to":[22,27,54,105,151,186,201,214,221],"automatically":[23],"assign":[24],"medical":[25,43,69],"codes":[26,185],"clinical":[28,63,157,190,227],"has":[30],"been":[31],"long-standing":[33],"active":[34],"research":[35],"area.":[36],"Despite":[37],"great":[39],"recent":[40],"progress":[41],"in":[42,61,245,265],"informatics":[44],"fueled":[45],"by":[46,197,252],"deep":[47,142,217],"learning,":[48],"it":[49,241],"is":[50,102,150,242],"still":[51],"challenge":[53],"find":[55],"the":[56,77,166,171,187,202,223,236],"specific":[57],"piece":[58],"evidence":[60],"note":[64,158],"which":[65,85,132,263],"justifies":[66],"particular":[68],"code":[70,129],"out":[71],"all":[73],"possible":[74],"codes.":[75,204,249],"Considering":[76],"large":[78],"amount":[79],"online":[81],"disease":[82,160],"knowledge":[83,118,136],"sources,":[84],"signs":[90],"and":[91,99,120,159,164,239],"symptoms":[92],"different":[94],"diseases,":[95],"risk":[97],"factors,":[98],"epidemiology,":[100],"there":[101],"an":[103,116],"opportunity":[104],"exploit":[106],"such":[107],"sources.":[108],"In":[109,250],"this":[110],"paper":[111],"we":[112,177,258],"consider":[113],"Wikipedia":[114,162,198,266],"as":[115],"external":[117,135],"source":[119],"propose":[121],"Knowledge":[122],"Source":[123],"Integration":[124],"(KSI),":[125],"novel":[127],"end-to-end":[128],"assignment":[130,181,246],"framework,":[131],"can":[133,259],"integrate":[134],"during":[137],"training":[138],"any":[140],"baseline":[141,172,208,237],"learning":[143,218],"model.":[144,173],"The":[145,229],"main":[146],"idea":[147],"KSI":[149,233,256],"calculate":[152],"matching":[153],"scores":[154,167],"between":[155],"related":[161],"documents,":[163],"combine":[165],"output":[169],"To":[174],"evaluate":[175],"KSI,":[176],"experimented":[178],"automatic":[180],"ICD-9":[183,203],"diagnosis":[184],"emergency":[188],"department":[189],"from":[192,211],"MIMIC-III":[193],"data":[194],"set,":[195],"aided":[196],"documents":[199,267],"corresponding":[200],"We":[205],"evaluated":[206],"several":[207],"models,":[209,257],"ranging":[210],"logistic":[212],"regression":[213],"recently":[215],"proposed":[216],"known":[220],"achieve":[222],"state-of-the-art":[224],"accuracy":[225],"on":[226],"notes.":[228],"results":[230],"show":[231],"that":[232,240],"consistently":[234],"improves":[235],"particularly":[243],"successful":[244],"rare":[248],"addition,":[251],"analyzing":[253],"weights":[254],"gain":[260],"understanding":[261],"words":[264],"provide":[268],"useful":[269],"predictions.":[272]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":3},{"year":2023,"cited_by_count":12},{"year":2022,"cited_by_count":10},{"year":2021,"cited_by_count":10},{"year":2020,"cited_by_count":6},{"year":2019,"cited_by_count":1}],"updated_date":"2026-04-04T16:13:02.066488","created_date":"2025-10-10T00:00:00"}