{"id":"https://openalex.org/W4408146517","doi":"https://doi.org/10.1109/icmla61862.2024.00041","title":"Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement Using Electronic Health Records","display_name":"Developing the Temporal Graph Convolutional Neural Network Model to Predict Hip Replacement Using Electronic Health Records","publication_year":2024,"publication_date":"2024-12-18","ids":{"openalex":"https://openalex.org/W4408146517","doi":"https://doi.org/10.1109/icmla61862.2024.00041"},"language":"en","primary_location":{"id":"doi:10.1109/icmla61862.2024.00041","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla61862.2024.00041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Machine Learning and Applications (ICMLA)","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/A5049234944","display_name":"Zoe Hancox","orcid":"https://orcid.org/0000-0003-0473-5971"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Zoe Hancox","raw_affiliation_strings":["School of Computer Science, University of Leeds,Leeds,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Leeds,Leeds,United Kingdom","institution_ids":["https://openalex.org/I130828816"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5009866045","display_name":"Sarah R. Kingsbury","orcid":"https://orcid.org/0000-0002-9917-1269"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]},{"id":"https://openalex.org/I4210114713","display_name":"NIHR Leeds Musculoskeletal Biomedical Research Unit","ror":"https://ror.org/035f5f914","country_code":"GB","type":"government","lineage":["https://openalex.org/I2799390153","https://openalex.org/I34931013","https://openalex.org/I4210114713"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Sarah R. Kingsbury","raw_affiliation_strings":["Leeds Institute of Rheumatic and Musculoskeletal Medicine (LIRMM), NIHR Leeds Biomedical Research Centre (BRC), University of Leeds,Leeds,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Leeds Institute of Rheumatic and Musculoskeletal Medicine (LIRMM), NIHR Leeds Biomedical Research Centre (BRC), University of Leeds,Leeds,United Kingdom","institution_ids":["https://openalex.org/I130828816","https://openalex.org/I4210114713"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069847534","display_name":"Andrew Clegg","orcid":"https://orcid.org/0000-0001-8938-7819"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Andrew Clegg","raw_affiliation_strings":["Bradford Institute for Health Research,Academic Unit for Ageing and Stroke Research,Bradford,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Bradford Institute for Health Research,Academic Unit for Ageing and Stroke Research,Bradford,United Kingdom","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100608680","display_name":"Philip G. Conaghan","orcid":"https://orcid.org/0000-0002-3478-5665"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Philip G. Conaghan","raw_affiliation_strings":["LIRMM, NIHR Leeds BRC, University of Leeds,Leeds,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"LIRMM, NIHR Leeds BRC, University of Leeds,Leeds,United Kingdom","institution_ids":["https://openalex.org/I130828816"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066256734","display_name":"Samuel D. Relton","orcid":"https://orcid.org/0000-0003-0634-4587"},"institutions":[{"id":"https://openalex.org/I130828816","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33","country_code":"GB","type":"education","lineage":["https://openalex.org/I130828816"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Samuel D. Relton","raw_affiliation_strings":["University of Leeds,Division of Health Services Research,Leeds,United Kingdom"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Leeds,Division of Health Services Research,Leeds,United Kingdom","institution_ids":["https://openalex.org/I130828816"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":9.2423,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.97880708,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":91,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"256","last_page":"263"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10512","display_name":"Orthopaedic implants and arthroplasty","score":0.9742000102996826,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10512","display_name":"Orthopaedic implants and arthroplasty","score":0.9742000102996826,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"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/T10562","display_name":"Total Knee Arthroplasty Outcomes","score":0.9350000023841858,"subfield":{"id":"https://openalex.org/subfields/2746","display_name":"Surgery"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7174319624900818},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6393998861312866},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.5231315493583679},{"id":"https://openalex.org/keywords/graph","display_name":"Graph","score":0.5152440667152405},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.3988218307495117},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.16196680068969727},{"id":"https://openalex.org/keywords/theoretical-computer-science","display_name":"Theoretical computer science","score":0.11067190766334534}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7174319624900818},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6393998861312866},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.5231315493583679},{"id":"https://openalex.org/C132525143","wikidata":"https://www.wikidata.org/wiki/Q141488","display_name":"Graph","level":2,"score":0.5152440667152405},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.3988218307495117},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.16196680068969727},{"id":"https://openalex.org/C80444323","wikidata":"https://www.wikidata.org/wiki/Q2878974","display_name":"Theoretical computer science","level":1,"score":0.11067190766334534},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/icmla61862.2024.00041","is_oa":false,"landing_page_url":"https://doi.org/10.1109/icmla61862.2024.00041","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 International Conference on Machine Learning and Applications (ICMLA)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[{"id":"https://openalex.org/G8409589362","display_name":null,"funder_award_id":"EP/S024336/1","funder_id":"https://openalex.org/F4320319990","funder_display_name":"National Institute for Health and Care Research"}],"funders":[{"id":"https://openalex.org/F4320319990","display_name":"National Institute for Health and Care Research","ror":"https://ror.org/0187kwz08"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":19,"referenced_works":["https://openalex.org/W2116341502","https://openalex.org/W2151189496","https://openalex.org/W2312931650","https://openalex.org/W2897738762","https://openalex.org/W2938932803","https://openalex.org/W2964959375","https://openalex.org/W3012441515","https://openalex.org/W3034222811","https://openalex.org/W3035816730","https://openalex.org/W3036485623","https://openalex.org/W3137229745","https://openalex.org/W4206482253","https://openalex.org/W4206528984","https://openalex.org/W4206841478","https://openalex.org/W4223540626","https://openalex.org/W4297199185","https://openalex.org/W4385193579","https://openalex.org/W4385670728","https://openalex.org/W4389670816"],"related_works":["https://openalex.org/W4391375266","https://openalex.org/W2899084033","https://openalex.org/W2748952813","https://openalex.org/W2390279801","https://openalex.org/W4391913857","https://openalex.org/W2358668433","https://openalex.org/W4396701345","https://openalex.org/W2376932109","https://openalex.org/W2001405890","https://openalex.org/W4396696052"],"abstract_inverted_index":{"Background:":[0],"Hip":[1],"replacement":[2,15,46,92,97,120,155,195],"procedures":[3],"improve":[4],"patient":[5,201],"lives":[6],"by":[7,21,101,197],"relieving":[8],"pain":[9,20],"and":[10,31,56,104,115,143,169,206],"restoring":[11],"mobility.":[12],"Predicting":[13],"hip":[14,45,91,96,119,154,194],"in":[16,49,123,159,200],"advance":[17,50,160],"could":[18],"reduce":[19],"enabling":[22],"timely":[23],"interventions,":[24],"prioritising":[25],"individuals":[26],"for":[27,39,134],"surgery":[28],"or":[29],"rehabilitation,":[30],"utilising":[32],"physiotherapy":[33],"to":[34,51,89,99],"potentially":[35,203],"delay":[36],"the":[37,127],"need":[38],"joint":[40],"replacement.":[41],"This":[42],"study":[43,142],"predicts":[44,118,153,193],"a":[47,178],"year":[48,122,158],"enhance":[52],"quality":[53],"of":[54,86,106,164,172,181,208],"life":[55],"health":[57],"service":[58],"efficiency.":[59],"Methods:":[60],"Adapting":[61],"previous":[62],"work":[63],"using":[64],"Temporal":[65],"Graph":[66],"Convolutional":[67],"Neural":[68],"Network":[69],"(TG-CNN)":[70],"models,":[71],"we":[72,138],"construct":[73],"temporal":[74],"graphs":[75],"from":[76,83],"primary":[77],"care":[78],"medical":[79],"event":[80],"codes,":[81],"sourced":[82],"ResearchOne":[84],"EHRs":[85],"40-75-year-old":[87],"patients,":[88],"predict":[90],"risk.":[93],"We":[94,125],"match":[95],"cases":[98,114],"controls":[100],"age,":[102],"sex,":[103],"Index":[105],"Multiple":[107],"Deprivation.":[108],"The":[109,189],"model,":[110],"trained":[111],"on":[112,129],"9,187":[113,116],"controls,":[117],"one":[121,157],"advance.":[124],"validate":[126],"model":[128,152,191],"two":[130],"unseen":[131],"datasets,":[132],"recalibrating":[133],"class":[135],"imbalance.":[136],"Additionally,":[137],"conduct":[139],"an":[140,162,170],"ablation":[141],"compare":[144],"against":[145],"four":[146],"baseline":[147],"models.":[148],"Results:":[149],"Our":[150],"best":[151],"risk":[156,196],"with":[161],"AUROC":[163],"0.724":[165],"(95%":[166,174,183],"CI:":[167,175,184],"0.715-0.733)":[168],"AUPRC":[171],"0.185":[173],"0.160-0.209),":[176],"achieving":[177],"calibration":[179],"slope":[180],"1.107":[182],"1.074-1.139)":[185],"after":[186],"recalibration.":[187],"Conclusions:":[188],"TG-CNN":[190],"effectively":[192],"identifying":[198],"patterns":[199],"trajectories,":[202],"improving":[204],"understanding":[205],"management":[207],"hip-related":[209],"conditions.":[210]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}