{"id":"https://openalex.org/W3008715188","doi":"https://doi.org/10.1109/bigdata47090.2019.9005657","title":"A Machine Learning Approach for Prediction of Length of Stay for the Kid\u2019s Inpatient Database","display_name":"A Machine Learning Approach for Prediction of Length of Stay for the Kid\u2019s Inpatient Database","publication_year":2019,"publication_date":"2019-12-01","ids":{"openalex":"https://openalex.org/W3008715188","doi":"https://doi.org/10.1109/bigdata47090.2019.9005657","mag":"3008715188"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata47090.2019.9005657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005657","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 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/A5102759025","display_name":"Shilpa Balan","orcid":"https://orcid.org/0000-0002-3582-2560"},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Shilpa Balan","raw_affiliation_strings":["Department of Information Systems, California State University, Los Angeles"],"affiliations":[{"raw_affiliation_string":"Department of Information Systems, California State University, Los Angeles","institution_ids":["https://openalex.org/I27825529"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5071506923","display_name":"Tanvi Gawade","orcid":null},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Tanvi Gawade","raw_affiliation_strings":["Department of Information Systems, California State University, Los Angeles"],"affiliations":[{"raw_affiliation_string":"Department of Information Systems, California State University, Los Angeles","institution_ids":["https://openalex.org/I27825529"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5008771581","display_name":"Aakanksha Tasgaonkar","orcid":null},"institutions":[{"id":"https://openalex.org/I27825529","display_name":"California State University Los Angeles","ror":"https://ror.org/0294hxs80","country_code":"US","type":"education","lineage":["https://openalex.org/I27825529"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Aakanksha Tasgaonkar","raw_affiliation_strings":["Department of Information Systems, California State University, Los Angeles"],"affiliations":[{"raw_affiliation_string":"Department of Information Systems, California State University, Los Angeles","institution_ids":["https://openalex.org/I27825529"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5102759025"],"corresponding_institution_ids":["https://openalex.org/I27825529"],"apc_list":null,"apc_paid":null,"fwci":0.5197,"has_fulltext":false,"cited_by_count":4,"citation_normalized_percentile":{"value":0.70937416,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":93,"max":96},"biblio":{"volume":"126","issue":null,"first_page":"5980","last_page":"5982"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11095","display_name":"Emergency and Acute Care Studies","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/T11095","display_name":"Emergency and Acute Care Studies","score":0.9918000102043152,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/T10391","display_name":"Healthcare Policy and Management","score":0.9585999846458435,"subfield":{"id":"https://openalex.org/subfields/2002","display_name":"Economics and Econometrics"},"field":{"id":"https://openalex.org/fields/20","display_name":"Economics, Econometrics and Finance"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12174","display_name":"Hospital Admissions and Outcomes","score":0.9524999856948853,"subfield":{"id":"https://openalex.org/subfields/2711","display_name":"Emergency Medicine"},"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/inpatient-care","display_name":"Inpatient care","score":0.546276330947876},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.5386497974395752},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.45197439193725586},{"id":"https://openalex.org/keywords/predictive-modelling","display_name":"Predictive modelling","score":0.4263654351234436},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.412799209356308},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.38993939757347107},{"id":"https://openalex.org/keywords/database","display_name":"Database","score":0.3802693486213684},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.2357119619846344},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.22932937741279602}],"concepts":[{"id":"https://openalex.org/C2775909303","wikidata":"https://www.wikidata.org/wiki/Q3259564","display_name":"Inpatient care","level":3,"score":0.546276330947876},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.5386497974395752},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.45197439193725586},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.4263654351234436},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.412799209356308},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38993939757347107},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.3802693486213684},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.2357119619846344},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.22932937741279602},{"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/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/bigdata47090.2019.9005657","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata47090.2019.9005657","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 Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.8700000047683716,"id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":13,"referenced_works":["https://openalex.org/W1510133588","https://openalex.org/W1985485648","https://openalex.org/W1994142752","https://openalex.org/W1998043931","https://openalex.org/W2015947155","https://openalex.org/W2017845497","https://openalex.org/W2042757011","https://openalex.org/W2060905884","https://openalex.org/W2120751691","https://openalex.org/W2131273356","https://openalex.org/W2137626471","https://openalex.org/W2156828749","https://openalex.org/W2180923296"],"related_works":["https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W3171520305","https://openalex.org/W1924178503","https://openalex.org/W3135126032","https://openalex.org/W4308716060","https://openalex.org/W3191198889","https://openalex.org/W4399767560","https://openalex.org/W1995617853"],"abstract_inverted_index":{"Due":[0],"to":[1,17,109],"a":[2,58],"high":[3],"mortality":[4],"rate":[5],"of":[6,34,37,68,75,88,104,112],"children":[7,84],"in":[8],"the":[9,19,35,48,66,102],"United":[10],"States,":[11],"there":[12],"is":[13,78],"an":[14,29],"immediate":[15],"need":[16],"analyze":[18],"pediatric":[20],"patient":[21],"care":[22],"information.":[23],"In":[24,53],"this":[25,44],"paper,":[26],"we":[27,46,56,99],"perform":[28],"exploratory":[30],"and":[31],"predictive":[32],"analysis":[33],"length":[36,67,87,111],"stay":[38,69,89,113],"on":[39],"kids'":[40],"inpatient":[41,51,71],"data.":[42],"For":[43],"study,":[45,55],"used":[47],"Kids'":[49],"2016":[50],"database.":[52],"our":[54,76],"developed":[57],"prediction":[59],"model":[60,77],"using":[61],"Random":[62],"Forest":[63],"Regression":[64],"for":[65,70,114],"kids.":[72,116],"The":[73],"accuracy":[74],"94.15%.":[79],"We":[80],"found":[81,100],"that":[82,101],"female":[83],"have":[85],"longer":[86,110],"as":[90],"they":[91],"are":[92],"more":[93],"at":[94],"risk":[95],"during":[96],"hospitalization.":[97],"Further,":[98],"number":[103],"complicated":[105],"births":[106],"could":[107],"lead":[108],"unborn":[115]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2021,"cited_by_count":2}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
