{"id":"https://openalex.org/W2006546373","doi":"https://doi.org/10.1109/bigdata.2014.7004241","title":"Predicting glaucoma progression using multi-task learning with heterogeneous features","display_name":"Predicting glaucoma progression using multi-task learning with heterogeneous features","publication_year":2014,"publication_date":"2014-10-01","ids":{"openalex":"https://openalex.org/W2006546373","doi":"https://doi.org/10.1109/bigdata.2014.7004241","mag":"2006546373"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata.2014.7004241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2014.7004241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"},"type":"article","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/A5062677262","display_name":"Shigeru Maya","orcid":"https://orcid.org/0000-0002-5314-3203"},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Shigeru Maya","raw_affiliation_strings":["Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114115269","display_name":"Kai Morino","orcid":null},"institutions":[{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kai Morino","raw_affiliation_strings":["Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5021981442","display_name":"Kenji Yamanishi","orcid":"https://orcid.org/0000-0001-7370-9991"},"institutions":[{"id":"https://openalex.org/I4210086780","display_name":"Japan Science and Technology Agency","ror":"https://ror.org/00097mb19","country_code":"JP","type":"government","lineage":["https://openalex.org/I4210086780"]},{"id":"https://openalex.org/I74801974","display_name":"The University of Tokyo","ror":"https://ror.org/057zh3y96","country_code":"JP","type":"education","lineage":["https://openalex.org/I74801974"]}],"countries":["JP"],"is_corresponding":false,"raw_author_name":"Kenji Yamanishi","raw_affiliation_strings":["Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan","JST-CREST, Saitama, Japan"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Graduate School of Information Science and Technology, University of Tokyo, Tokyo, Japan","institution_ids":["https://openalex.org/I74801974"]},{"raw_affiliation_string":"JST-CREST, Saitama, Japan","institution_ids":["https://openalex.org/I4210086780"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":1.1025,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.77937226,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":96},"biblio":{"volume":"17","issue":null,"first_page":"261","last_page":"270"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11438","display_name":"Retinal Imaging and Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T11438","display_name":"Retinal Imaging and Analysis","score":0.9997000098228455,"subfield":{"id":"https://openalex.org/subfields/2741","display_name":"Radiology, Nuclear Medicine and Imaging"},"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/T10250","display_name":"Glaucoma and retinal disorders","score":0.9984999895095825,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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/T10170","display_name":"Retinal Diseases and Treatments","score":0.9829000234603882,"subfield":{"id":"https://openalex.org/subfields/2731","display_name":"Ophthalmology"},"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":0.7407606840133667},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6271989345550537},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5812798738479614},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.5615447163581848},{"id":"https://openalex.org/keywords/matrix-decomposition","display_name":"Matrix decomposition","score":0.5485015511512756},{"id":"https://openalex.org/keywords/multi-task-learning","display_name":"Multi-task learning","score":0.5309585928916931},{"id":"https://openalex.org/keywords/similarity","display_name":"Similarity (geometry)","score":0.5303690433502197},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.5241023302078247},{"id":"https://openalex.org/keywords/field","display_name":"Field (mathematics)","score":0.5056111812591553},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.48746585845947266},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.4298853278160095},{"id":"https://openalex.org/keywords/measure","display_name":"Measure (data warehouse)","score":0.42365992069244385},{"id":"https://openalex.org/keywords/feature-extraction","display_name":"Feature extraction","score":0.41716018319129944},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4122200310230255},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.344440221786499},{"id":"https://openalex.org/keywords/image","display_name":"Image (mathematics)","score":0.18880215287208557},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.17944645881652832}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7407606840133667},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6271989345550537},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5812798738479614},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.5615447163581848},{"id":"https://openalex.org/C42355184","wikidata":"https://www.wikidata.org/wiki/Q1361088","display_name":"Matrix decomposition","level":3,"score":0.5485015511512756},{"id":"https://openalex.org/C28006648","wikidata":"https://www.wikidata.org/wiki/Q6934509","display_name":"Multi-task learning","level":3,"score":0.5309585928916931},{"id":"https://openalex.org/C103278499","wikidata":"https://www.wikidata.org/wiki/Q254465","display_name":"Similarity (geometry)","level":3,"score":0.5303690433502197},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.5241023302078247},{"id":"https://openalex.org/C9652623","wikidata":"https://www.wikidata.org/wiki/Q190109","display_name":"Field (mathematics)","level":2,"score":0.5056111812591553},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.48746585845947266},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.4298853278160095},{"id":"https://openalex.org/C2780009758","wikidata":"https://www.wikidata.org/wiki/Q6804172","display_name":"Measure (data warehouse)","level":2,"score":0.42365992069244385},{"id":"https://openalex.org/C52622490","wikidata":"https://www.wikidata.org/wiki/Q1026626","display_name":"Feature extraction","level":2,"score":0.41716018319129944},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4122200310230255},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.344440221786499},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","level":2,"score":0.18880215287208557},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.17944645881652832},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"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/C187736073","wikidata":"https://www.wikidata.org/wiki/Q2920921","display_name":"Management","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/C158693339","wikidata":"https://www.wikidata.org/wiki/Q190524","display_name":"Eigenvalues and eigenvectors","level":2,"score":0.0},{"id":"https://openalex.org/C202444582","wikidata":"https://www.wikidata.org/wiki/Q837863","display_name":"Pure mathematics","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}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata.2014.7004241","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata.2014.7004241","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2014 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320912","display_name":"Ministry of Education, Culture, Sports, Science and Technology","ror":"https://ror.org/048rj2z13"},{"id":"https://openalex.org/F4320322832","display_name":"University of Tokyo","ror":"https://ror.org/057zh3y96"},{"id":"https://openalex.org/F4320334764","display_name":"Japan Society for the Promotion of Science","ror":"https://ror.org/00hhkn466"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1510073064","https://openalex.org/W1544449255","https://openalex.org/W1942758450","https://openalex.org/W1971307050","https://openalex.org/W1983873315","https://openalex.org/W2042775700","https://openalex.org/W2049588365","https://openalex.org/W2098841537","https://openalex.org/W2105869342","https://openalex.org/W2116413942","https://openalex.org/W2122090912","https://openalex.org/W2124609748","https://openalex.org/W2148603752","https://openalex.org/W2150621701","https://openalex.org/W2165395308","https://openalex.org/W2913340405","https://openalex.org/W2914746235","https://openalex.org/W6632433782","https://openalex.org/W6640786210","https://openalex.org/W6677204830","https://openalex.org/W6677671969","https://openalex.org/W6684295950"],"related_works":["https://openalex.org/W2375480909","https://openalex.org/W2353314428","https://openalex.org/W2012019886","https://openalex.org/W2166090428","https://openalex.org/W2381021552","https://openalex.org/W2354749003","https://openalex.org/W2377121353","https://openalex.org/W2350529538","https://openalex.org/W4308090169","https://openalex.org/W1972390760"],"abstract_inverted_index":{"We":[0,83,150],"consider":[1],"the":[2,21,30,48,85,101,110,115,147,161],"prediction":[3,86],"of":[4,32,50,71,90,114,123],"glaucomatous":[5],"visual":[6,77,138],"field":[7,139],"loss":[8,78,140],"based":[9],"on":[10],"patient":[11,36,53,74],"datasets.":[12],"It":[13],"is":[14,23,37],"critically":[15],"important":[16],"to":[17,65,118],"predict":[18],"how":[19],"rapidly":[20],"disease":[22],"progressing":[24],"in":[25,100],"an":[26],"individual":[27],"patient.":[28],"However,":[29],"number":[31],"measurements":[33],"for":[34,93,137,146],"each":[35],"so":[38],"small":[39],"that":[40,108,153],"a":[41,51,60,120,134,142],"reliable":[42],"predictor":[43],"cannot":[44],"be":[45],"constructed":[46],"from":[47],"data":[49,69],"single":[52],"alone.":[54],"In":[55],"this":[56,66],"paper,":[57],"we":[58,104,129],"propose":[59],"novel":[61],"multi-task":[62],"learning":[63],"approach":[64],"issue.":[67],"Patient":[68],"consist":[70],"three":[72,102],"features:":[73],"ID,":[75],"74-dimensional":[76],"values,":[79],"and":[80,141],"inspection":[81],"time.":[82],"reduce":[84],"problem":[87],"into":[88],"one":[89],"matrix":[91,124],"completion":[92],"these":[94],"features.":[95],"Specifically,":[96],"by":[97],"assuming":[98],"heterogeneity":[99],"features,":[103],"introduce":[105],"similarity":[106,135],"measures":[107],"reflect":[109],"unique":[111],"statistical":[112],"nature":[113],"respective":[116],"features":[117],"solve":[119],"specific":[121],"type":[122],"decomposition":[125],"problem.":[126],"For":[127],"example,":[128],"employ":[130],"Gaussian":[131],"kernels":[132],"as":[133],"measure":[136],"linear":[143],"regression-type":[144],"relation":[145],"time":[148],"feature.":[149],"empirically":[151],"demonstrate":[152],"our":[154],"proposed":[155],"method":[156],"works":[157],"significantly":[158],"better":[159],"than":[160],"existing":[162],"methods.":[163]},"counts_by_year":[{"year":2019,"cited_by_count":1},{"year":2018,"cited_by_count":1},{"year":2017,"cited_by_count":1},{"year":2016,"cited_by_count":2},{"year":2015,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
