{"id":"https://openalex.org/W2991205545","doi":"https://doi.org/10.1109/ichi.2019.8904791","title":"Using Temporal Feature Aggregation and Gradient Boosting Tree on Missing Data Imputation","display_name":"Using Temporal Feature Aggregation and Gradient Boosting Tree on Missing Data Imputation","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2991205545","doi":"https://doi.org/10.1109/ichi.2019.8904791","mag":"2991205545"},"language":"en","primary_location":{"id":"doi:10.1109/ichi.2019.8904791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichi.2019.8904791","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Healthcare Informatics (ICHI)","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/A5033101822","display_name":"Yanni Kang","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141029","display_name":"China National Health Development Research Center","ror":"https://ror.org/043648k83","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141029"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Yanni Kang","raw_affiliation_strings":["PingAn Health Technology,Beijing,China","PingAn Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"PingAn Health Technology,Beijing,China","institution_ids":["https://openalex.org/I4210141029"]},{"raw_affiliation_string":"PingAn Health Technology, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5014205869","display_name":"Xiaoyu Jia","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141029","display_name":"China National Health Development Research Center","ror":"https://ror.org/043648k83","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiaoyu Jia","raw_affiliation_strings":["PingAn Health Technology,Beijing,China","PingAn Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"PingAn Health Technology,Beijing,China","institution_ids":["https://openalex.org/I4210141029"]},{"raw_affiliation_string":"PingAn Health Technology, Beijing, China","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100394767","display_name":"Xiang Li","orcid":"https://orcid.org/0000-0002-0761-0931"},"institutions":[{"id":"https://openalex.org/I4210141029","display_name":"China National Health Development Research Center","ror":"https://ror.org/043648k83","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Xiang Li","raw_affiliation_strings":["PingAn Health Technology,Beijing,China","PingAn Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"PingAn Health Technology,Beijing,China","institution_ids":["https://openalex.org/I4210141029"]},{"raw_affiliation_string":"PingAn Health Technology, Beijing, China","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5023775139","display_name":"Guotong Xie","orcid":null},"institutions":[{"id":"https://openalex.org/I4210141029","display_name":"China National Health Development Research Center","ror":"https://ror.org/043648k83","country_code":"CN","type":"healthcare","lineage":["https://openalex.org/I4210141029"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Guotong Xie","raw_affiliation_strings":["PingAn Health Technology,Beijing,China","PingAn Health Technology, Beijing, China"],"affiliations":[{"raw_affiliation_string":"PingAn Health Technology,Beijing,China","institution_ids":["https://openalex.org/I4210141029"]},{"raw_affiliation_string":"PingAn Health Technology, Beijing, China","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033101822"],"corresponding_institution_ids":["https://openalex.org/I4210141029"],"apc_list":null,"apc_paid":null,"fwci":0.14,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58745238,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"2"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9980000257492065,"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":0.9980000257492065,"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/T10136","display_name":"Statistical Methods and Inference","score":0.992900013923645,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T10243","display_name":"Statistical Methods and Bayesian Inference","score":0.968999981880188,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/missing-data","display_name":"Missing data","score":0.9501618146896362},{"id":"https://openalex.org/keywords/imputation","display_name":"Imputation (statistics)","score":0.8695600032806396},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6448109149932861},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.6376323699951172},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.4629662036895752},{"id":"https://openalex.org/keywords/gradient-boosting","display_name":"Gradient boosting","score":0.4441746175289154},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.34713780879974365},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.33457547426223755},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.21671345829963684},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18947729468345642},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.1576523780822754}],"concepts":[{"id":"https://openalex.org/C9357733","wikidata":"https://www.wikidata.org/wiki/Q6878417","display_name":"Missing data","level":2,"score":0.9501618146896362},{"id":"https://openalex.org/C58041806","wikidata":"https://www.wikidata.org/wiki/Q1660484","display_name":"Imputation (statistics)","level":3,"score":0.8695600032806396},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6448109149932861},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.6376323699951172},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.4629662036895752},{"id":"https://openalex.org/C70153297","wikidata":"https://www.wikidata.org/wiki/Q5591907","display_name":"Gradient boosting","level":3,"score":0.4441746175289154},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.34713780879974365},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.33457547426223755},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.21671345829963684},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18947729468345642},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.1576523780822754}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ichi.2019.8904791","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichi.2019.8904791","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Healthcare Informatics (ICHI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":5,"referenced_works":["https://openalex.org/W1678356000","https://openalex.org/W2396881363","https://openalex.org/W2768348081","https://openalex.org/W2771817472","https://openalex.org/W6745609711"],"related_works":["https://openalex.org/W2181530120","https://openalex.org/W4211215373","https://openalex.org/W2024529227","https://openalex.org/W2055961818","https://openalex.org/W1574575415","https://openalex.org/W3144172081","https://openalex.org/W3179858851","https://openalex.org/W3028371478","https://openalex.org/W2081476516","https://openalex.org/W2581984549"],"abstract_inverted_index":{"The":[0,130],"goal":[1],"of":[2,47,121,165],"the":[3,11,20,33,109,119,122,127,139,146,151,162,177,181,186,193,198],"7th":[4],"IEEE":[5],"ICHI":[6],"Challenge":[7],"is":[8,32,69],"to":[9,15,125,196],"find":[10],"most":[12,34],"appropriate":[13],"method":[14],"impute":[16,197],"missing":[17,60,67,89,128,131,154,178,194,199],"data":[18,25,42,61,200],"in":[19,37],"longitudinal":[21],"ICU":[22],"laboratory":[23],"test":[24],"derived":[26],"from":[27],"MIMIC-III":[28],"[1].":[29],"Missing":[30],"data/measurements":[31],"common":[35],"problem":[36],"clinical":[38,41],"data,":[39],"because":[40],"series":[43],"contains":[44],"many":[45],"streams":[46],"measurements":[48,68],"that":[49,189],"are":[50,134,156,159],"sampled":[51],"at":[52,62],"multiple":[53],"and":[54,77,80,114,145,158,184],"irregular":[55],"time,":[56],"which":[57],"would":[58],"generate":[59],"different":[63,157],"rates.":[64],"However,":[65,150],"these":[66],"often":[70],"critical":[71],"for":[72,98,153],"accurate":[73,78],"diagnosis,":[74],"prognosis,":[75],"treatment,":[76],"modeling":[79],"statistical":[81],"analyses":[82],"as":[83,118],"well.":[84],"In":[85],"this":[86],"challenge,":[87],"during":[88],"value":[90,179],"imputation":[91,167,174],"process,":[92],"we":[93,104],"trained":[94],"13":[95],"models":[96],"independently":[97],"each":[99,143],"feature.":[100],"For":[101],"every":[102],"model,":[103],"extracted":[105],"200":[106],"features":[107,120],"including":[108],"measurements,":[110],"statistics,":[111],"time":[112,116],"intervals":[113],"other":[115],"information":[117],"predict":[123],"model":[124,163,188],"capture":[126],"data.":[129],"target":[132],"values":[133,155],"interpolated":[135],"by":[136,161],"using":[137,185],"both":[138],"temporal":[140],"relationships":[141,147],"within":[142],"stream":[144],"across":[148],"streams.":[149],"replacements":[152],"determined":[160],"performance":[164],"distinct":[166],"strategies.":[168],"This":[169],"current":[170],"study":[171],"utilized":[172],"two":[173],"methods:":[175],"replacing":[176],"with":[180],"adjacent":[182],"measurement,":[183],"tree":[187],"can":[190],"automatically":[191],"identify":[192],"patterns":[195],"when":[201],"needed.":[202]},"counts_by_year":[{"year":2019,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
