{"id":"https://openalex.org/W3188449234","doi":"https://doi.org/10.1145/3459930.3469490","title":"Using electronic health records to accurately predict COVID-19 health outcomes through a novel machine learning pipeline","display_name":"Using electronic health records to accurately predict COVID-19 health outcomes through a novel machine learning pipeline","publication_year":2021,"publication_date":"2021-07-30","ids":{"openalex":"https://openalex.org/W3188449234","doi":"https://doi.org/10.1145/3459930.3469490","mag":"3188449234"},"language":"en","primary_location":{"id":"doi:10.1145/3459930.3469490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459930.3469490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","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/A5053197234","display_name":"Alice Feng","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alice Feng","raw_affiliation_strings":["The Harker School"],"affiliations":[{"raw_affiliation_string":"The Harker School","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5053197234"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2719,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.62699826,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"1"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9994999766349792,"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":0.9994999766349792,"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/T11775","display_name":"COVID-19 diagnosis using AI","score":0.9990000128746033,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9794999957084656,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7381541728973389},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7004027366638184},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6908276081085205},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"Feature engineering","score":0.6023989915847778},{"id":"https://openalex.org/keywords/normalization","display_name":"Normalization (sociology)","score":0.5887326002120972},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5518643260002136},{"id":"https://openalex.org/keywords/feature-selection","display_name":"Feature selection","score":0.5091103911399841},{"id":"https://openalex.org/keywords/coronavirus-disease-2019","display_name":"Coronavirus disease 2019 (COVID-19)","score":0.4967713952064514},{"id":"https://openalex.org/keywords/feature","display_name":"Feature (linguistics)","score":0.4745030105113983},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.4545728862285614},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.4412640929222107},{"id":"https://openalex.org/keywords/data-set","display_name":"Data set","score":0.4363560378551483},{"id":"https://openalex.org/keywords/medical-diagnosis","display_name":"Medical diagnosis","score":0.41589680314064026},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3811624348163605},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.2312922477722168},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.21013608574867249},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.17970648407936096}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7381541728973389},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7004027366638184},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6908276081085205},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.6023989915847778},{"id":"https://openalex.org/C136886441","wikidata":"https://www.wikidata.org/wiki/Q926129","display_name":"Normalization (sociology)","level":2,"score":0.5887326002120972},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5518643260002136},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.5091103911399841},{"id":"https://openalex.org/C3008058167","wikidata":"https://www.wikidata.org/wiki/Q84263196","display_name":"Coronavirus disease 2019 (COVID-19)","level":4,"score":0.4967713952064514},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.4745030105113983},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.4545728862285614},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.4412640929222107},{"id":"https://openalex.org/C58489278","wikidata":"https://www.wikidata.org/wiki/Q1172284","display_name":"Data set","level":2,"score":0.4363560378551483},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.41589680314064026},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3811624348163605},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.2312922477722168},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.21013608574867249},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.17970648407936096},{"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},{"id":"https://openalex.org/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0},{"id":"https://openalex.org/C41895202","wikidata":"https://www.wikidata.org/wiki/Q8162","display_name":"Linguistics","level":1,"score":0.0},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.0},{"id":"https://openalex.org/C19165224","wikidata":"https://www.wikidata.org/wiki/Q23404","display_name":"Anthropology","level":1,"score":0.0},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0},{"id":"https://openalex.org/C144024400","wikidata":"https://www.wikidata.org/wiki/Q21201","display_name":"Sociology","level":0,"score":0.0},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.0},{"id":"https://openalex.org/C524204448","wikidata":"https://www.wikidata.org/wiki/Q788926","display_name":"Infectious disease (medical specialty)","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3459930.3469490","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3459930.3469490","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 12th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.7300000190734863}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":2,"referenced_works":["https://openalex.org/W2171033594","https://openalex.org/W3014524604"],"related_works":["https://openalex.org/W2953716828","https://openalex.org/W2904857019","https://openalex.org/W2944728705","https://openalex.org/W3011538607","https://openalex.org/W2904022177","https://openalex.org/W2591697403","https://openalex.org/W4321441197","https://openalex.org/W2359348847","https://openalex.org/W4360615883","https://openalex.org/W2885170888"],"abstract_inverted_index":{"Current":[0],"COVID-19":[1,15,24,46,136,143],"predictive":[2],"models":[3,82],"primarily":[4],"focus":[5],"on":[6,14,97,130],"predicting":[7],"the":[8,116],"risk":[9,144],"of":[10],"mortality,":[11,51],"and":[12,63,76,93],"rely":[13],"specific":[16],"medical":[17,131],"data":[18,65,69,118,132],"such":[19],"as":[20,127],"chest":[21],"imaging":[22],"after":[23,135],"diagnosis.":[25,137],"In":[26,58,138],"this":[27],"project,":[28],"we":[29,60,80,140],"developed":[30,61,110],"an":[31],"innovative":[32],"supervised":[33],"machine":[34,85,102],"learning":[35,86,103],"pipeline":[36,104],"using":[37,83,115],"longitudinal":[38],"Electronic":[39],"Health":[40],"Records":[41],"(EHR)":[42],"to":[43],"accurately":[44],"predict":[45],"related":[47],"health":[48],"outcomes":[49],"including":[50,68],"ventilation,":[52],"days":[53],"in":[54],"hospital":[55],"or":[56],"ICU.":[57],"particular,":[59],"unique":[62],"effective":[64],"processing":[66],"algorithms,":[67],"cleaning,":[70],"initial":[71],"feature":[72,77,94],"screening,":[73],"vector":[74],"representation,":[75],"normalization.":[78],"Then":[79],"trained":[81,129],"state-of-the-art":[84],"strategies":[87],"combined":[88],"with":[89,149],"different":[90],"parameter":[91],"settings":[92],"selection.":[95],"Based":[96],"routinely":[98],"collected":[99],"EHR,":[100],"our":[101],"not":[105],"only":[106,134],"consistently":[107],"outperformed":[108],"those":[109,128],"by":[111],"other":[112],"research":[113],"groups":[114],"same":[117],"set,":[119],"but":[120],"also":[121],"achieved":[122],"similar":[123],"mortality":[124],"prediction":[125],"accuracy":[126],"available":[133],"addition,":[139],"identified":[141],"top":[142],"factors,":[145],"which":[146],"are":[147],"consistent":[148],"epidemiologic":[150],"findings.":[151]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}