{"id":"https://openalex.org/W7082321917","doi":null,"title":"An Interpretable Multimodal Framework for Regional Organ Transplantation Outcomes","display_name":"An Interpretable Multimodal Framework for Regional Organ Transplantation Outcomes","publication_year":2025,"publication_date":"2025-05-01","ids":{"openalex":"https://openalex.org/W7082321917"},"language":"en","primary_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/162752","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/162752","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/162752/1/lee-juylee24-meng-eecs-2025-thesis.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://dspace.mit.edu/bitstream/1721.1/162752/1/lee-juylee24-meng-eecs-2025-thesis.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":null,"display_name":"Lee, Ju Young","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Lee, Ju Young","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":true,"primary_topic":{"id":"https://openalex.org/T11163","display_name":"Organ Donation and Transplantation","score":0.5619999766349792,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T11163","display_name":"Organ Donation and Transplantation","score":0.5619999766349792,"subfield":{"id":"https://openalex.org/subfields/2739","display_name":"Public Health, Environmental and Occupational Health"},"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.1550000011920929,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.08919999748468399,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/kidney-transplantation","display_name":"kidney transplantation","score":0.9139999747276306},{"id":"https://openalex.org/keywords/organ-allocation","display_name":"organ allocation","score":0.46299999952316284},{"id":"https://openalex.org/keywords/large-language-model-embeddings","display_name":"large language model embeddings","score":0.7760000228881836},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.7699999809265137},{"id":"https://openalex.org/keywords/data-sparsity","display_name":"data sparsity","score":0.6740000247955322},{"id":"https://openalex.org/keywords/organ-shortage","display_name":"organ shortage","score":0.4230000078678131},{"id":"https://openalex.org/keywords/explainable-ai","display_name":"explainable AI","score":0.6610000133514404},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.5270000100135803},{"id":"https://openalex.org/keywords/decision-support-system","display_name":"decision support system","score":0.6240000128746033},{"id":"https://openalex.org/keywords/transformer","display_name":"transformer models","score":0.34599998593330383}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8075000047683716},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.5946000218391418},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5555999875068665},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.539900004863739},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.5260999798774719},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49399998784065247},{"id":"https://openalex.org/C2780084366","wikidata":"https://www.wikidata.org/wiki/Q37732","display_name":"Demographics","level":2,"score":0.48190000653266907},{"id":"https://openalex.org/C8642999","wikidata":"https://www.wikidata.org/wiki/Q4171168","display_name":"Hyperparameter","level":2,"score":0.453000009059906},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3856000006198883},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.34549999237060547},{"id":"https://openalex.org/C2911091166","wikidata":"https://www.wikidata.org/wiki/Q106419912","display_name":"Transplantation","level":2,"score":0.33149999380111694},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3127000033855438},{"id":"https://openalex.org/C2780303639","wikidata":"https://www.wikidata.org/wiki/Q740909","display_name":"Kidney transplantation","level":3,"score":0.30300000309944153},{"id":"https://openalex.org/C51632099","wikidata":"https://www.wikidata.org/wiki/Q3985153","display_name":"Training set","level":2,"score":0.29739999771118164},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28369998931884766},{"id":"https://openalex.org/C2994498544","wikidata":"https://www.wikidata.org/wiki/Q740909","display_name":"Kidney transplant","level":4,"score":0.2802000045776367},{"id":"https://openalex.org/C4441509","wikidata":"https://www.wikidata.org/wiki/Q6418787","display_name":"Multimodal therapy","level":2,"score":0.2651999890804291},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C2777267654","wikidata":"https://www.wikidata.org/wiki/Q3519023","display_name":"Test (biology)","level":2,"score":0.2522999942302704}],"mesh":[],"locations_count":1,"locations":[{"id":"pmh:oai:dspace.mit.edu:1721.1/162752","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/162752","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/162752/1/lee-juylee24-meng-eecs-2025-thesis.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"}],"best_oa_location":{"id":"pmh:oai:dspace.mit.edu:1721.1/162752","doi":null,"is_oa":true,"landing_page_url":"https://hdl.handle.net/1721.1/162752","pdf_url":"https://dspace.mit.edu/bitstream/1721.1/162752/1/lee-juylee24-meng-eecs-2025-thesis.pdf","source":{"id":"https://openalex.org/S4306400425","display_name":"DSpace@MIT (Massachusetts Institute of Technology)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I63966007","host_organization_name":"Massachusetts Institute of Technology","host_organization_lineage":["https://openalex.org/I63966007"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by-nc","license_id":"https://openalex.org/licenses/cc-by-nc","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Thesis"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8048999905586243}],"study_designs":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320308349","display_name":"Massachusetts General Hospital","ror":"https://ror.org/002pd6e78"}],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7082321917.pdf","grobid_xml":"https://content.openalex.org/works/W7082321917.grobid-xml"},"referenced_works_count":29,"referenced_works":["https://openalex.org/W2072761036","https://openalex.org/W2074769157","https://openalex.org/W2153943197","https://openalex.org/W2162113539","https://openalex.org/W2233393753","https://openalex.org/W2295598076","https://openalex.org/W2470947933","https://openalex.org/W2615418449","https://openalex.org/W2618851150","https://openalex.org/W2626778328","https://openalex.org/W2925863688","https://openalex.org/W2988089865","https://openalex.org/W3205068155","https://openalex.org/W4211206946","https://openalex.org/W4283318146","https://openalex.org/W4285012884","https://openalex.org/W4297253404","https://openalex.org/W4307001389","https://openalex.org/W4307539314","https://openalex.org/W4312141104","https://openalex.org/W4313530672","https://openalex.org/W4380090567","https://openalex.org/W4386348049","https://openalex.org/W4388033286","https://openalex.org/W4403628685","https://openalex.org/W4411542050","https://openalex.org/W7133183334","https://openalex.org/W7133184119","https://openalex.org/W7168387555"],"related_works":["https://openalex.org/W7123789178","https://openalex.org/W4414285433","https://openalex.org/W7119495756","https://openalex.org/W4401102282","https://openalex.org/W7214981200","https://openalex.org/W7127566790","https://openalex.org/W4388127156","https://openalex.org/W4416873735","https://openalex.org/W1970050658","https://openalex.org/W4414418615"],"abstract_inverted_index":{"The":[0],"demand":[1,342],"for":[2,63,190,311],"kidney":[3,64,345],"transplants":[4,23],"continues":[5],"to":[6,54,212,260,288],"outpace":[7],"supply,":[8],"with":[9,50,99,114,122,129,147,178],"over":[10],"89,792":[11],"patients":[12],"on":[13,76,92,107,162],"the":[14,41,77,137,144,158,166,174,198,224,228,241,244,315,336,344],"waitlist":[15],"as":[16],"of":[17,30,43,79,105,151,155,182,186,200,243,291,317],"September":[18],"2024,":[19],"yet":[20],"only":[21],"27,332":[22],"performed":[24],"in":[25,133,157,170,216,235,314,343],"2023":[26],"[1],":[27],"and":[28,103,153,184,206,222,247,281,293,320,327,341],"28%":[29],"recovered":[31],"kidneys":[32],"going":[33],"non-utilized":[34],"[2].":[35],"In":[36,219],"this":[37,163,306],"thesis,":[38],"we":[39,72,265,285],"highlight":[40],"use":[42],"large":[44],"language":[45],"model":[46,70,169,214,226,262,295],"(LLM)":[47],"embeddings":[48],"combined":[49],"structured":[51],"tabular":[52,280],"data":[53,106,233],"build":[55],"a":[56,84,148,179,267],"predictive":[57,69],"classifier":[58],"that":[59,196,232,248,271],"estimates":[60],"offer":[61],"outcomes":[62],"donor-recipient":[65],"matches.":[66],"For":[67],"each":[68],"deployed,":[71],"provide":[73],"further":[74,249],"analysis":[75,87],"interpretability":[78],"these":[80,236],"black-box":[81],"models":[82,319],"using":[83],"custom-designed":[85],"SHAP":[86,269],"framework.":[88],"Our":[89,193],"study":[90],"focuses":[91],"three":[93],"distinct":[94],"U.S.":[95],"regions":[96,237,290],"(Regions":[97],"1,2,3)":[98],"markedly":[100],"different":[101],"demographics":[102],"amounts":[104],"organ":[108],"acceptances":[109],"(Region":[110],"1:":[111],"43,126":[112],"offers":[113,121],"2.19%":[115],"acceptance":[116,124,131],"rate,":[117,125],"Region":[118,126,141,171,217],"2:":[119],"394,640":[120],"1.57%":[123],"3:":[127],"169,342":[128],"2.23%":[130],"rate":[132],"years":[134],"2016-2019).":[135],"Among":[136],"baseline":[138,225],"XGBoost":[139],"models,":[140],"3":[142,172],"achieved":[143,173],"highest":[145],"performance,":[146],"precision-accept":[149,180],"score":[150,181],"0.929":[152],"accuracy":[154,185],"0.993":[156,187],"test":[159],"data.":[160,283],"Building":[161],"strong":[164],"foundation,":[165],"multimodal":[167,245,276],"TabText":[168,229],"best":[175],"performance":[176,215],"overall,":[177],"0.959":[183],"after":[188],"fine-tuning":[189],"six":[191],"epochs.":[192],"findings":[194],"suggest":[195],"increasing":[197],"number":[199],"text":[201],"features,":[202],"extending":[203],"training":[204],"epochs,":[205],"incorporating":[207],"explicit":[208],"numerical":[209],"values":[210],"led":[211],"improved":[213],"3.":[218],"Regions":[220],"1":[221],"2,":[223],"outperformed":[227],"model,":[230],"suggesting":[231],"sparsity":[234],"may":[238],"have":[239],"limited":[240],"effectiveness":[242],"approach":[246],"hyperparameter":[250],"tuning":[251],"is":[252],"needed.":[253],"We":[254],"also":[255],"present":[256],"several":[257],"visualization":[258],"techniques":[259],"enhance":[261],"interpretability.":[263],"Specifically,":[264],"developed":[266],"novel":[268],"explainer":[270],"illustrates":[272],"feature":[273],"interactions":[274],"between":[275,339],"inputs,":[277],"including":[278],"both":[279],"textual":[282],"Additionally,":[284],"explored":[286],"methods":[287],"identify":[289],"high":[292],"low":[294],"fidelity":[296],"by":[297],"mapping":[298],"per-sample":[299],"prediction":[300],"errors":[301],"onto":[302],"t-SNE":[303],"embeddings.":[304],"Overall,":[305],"thesis":[307],"introduces":[308],"new":[309],"directions":[310],"transplant":[312,346],"research":[313],"context":[316],"transformer-based":[318],"interpretable":[321],"AI.":[322],"Leveraging":[323],"data-driven":[324],"decision-support":[325],"tools":[326],"refining":[328],"allocation":[329],"policies":[330],"are":[331],"essential":[332],"steps":[333],"toward":[334],"addressing":[335],"persistent":[337],"gap":[338],"supply":[340],"landscape.":[347]},"counts_by_year":[],"updated_date":"2026-11-07T11:25:07.637180","created_date":"2025-10-10T00:00:00"}
