{"id":"https://openalex.org/W3203093501","doi":"https://doi.org/10.48550/arxiv.2109.12323","title":"Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?","display_name":"Deep Learning-Based Detection of the Acute Respiratory Distress Syndrome: What Are the Models Learning?","publication_year":2021,"publication_date":"2021-09-25","ids":{"openalex":"https://openalex.org/W3203093501","doi":"https://doi.org/10.48550/arxiv.2109.12323","mag":"3203093501"},"language":"en","primary_location":{"id":"pmh:oai:arXiv.org:2109.12323","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.12323","pdf_url":"https://arxiv.org/pdf/2109.12323","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"volume":null,"type":"preprint","indexed_in":["arxiv","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/2109.12323","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5030964696","display_name":"Gregory B. Rehm","orcid":"https://orcid.org/0000-0003-4717-4166","observed_orcids":["https://orcid.org/0000-0003-4717-4166"]},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rehm, Gregory B.","raw_affiliation_strings":["Univ. of California Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of California Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100407036","display_name":"Chao Wang","orcid":"https://orcid.org/0000-0002-7460-7628","observed_orcids":["https://orcid.org/0000-0002-7460-7628"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Wang, Chao","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5055356324","display_name":"Irene Cort\u00e9s\u2010Puch","orcid":"https://orcid.org/0000-0002-3639-5046","observed_orcids":["https://orcid.org/0000-0002-3639-5046"]},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Cortes-Puch, Irene","raw_affiliation_strings":["Univ. of California Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of California Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017383502","display_name":"Chen\u2010Nee Chuah","orcid":"https://orcid.org/0000-0002-2772-387X","observed_orcids":["https://orcid.org/0000-0002-2772-387X"]},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuah, Chen-Nee","raw_affiliation_strings":["Univ. of California Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of California Davis","institution_ids":["https://openalex.org/I84218800"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5084163286","display_name":"Jason Y. Adams","orcid":"https://orcid.org/0000-0001-5623-8702","observed_orcids":["https://orcid.org/0000-0001-5623-8702"]},"institutions":[{"id":"https://openalex.org/I84218800","display_name":"University of California, Davis","ror":"https://ror.org/05rrcem69","country_code":"US","type":"education","lineage":["https://openalex.org/I84218800"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Adams, Jason","raw_affiliation_strings":["Univ. of California Davis"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Univ. of California Davis","institution_ids":["https://openalex.org/I84218800"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"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":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.5785999894142151,"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":0.5785999894142151,"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/T10322","display_name":"Respiratory Support and Mechanisms","score":0.2687999904155731,"subfield":{"id":"https://openalex.org/subfields/2740","display_name":"Pulmonary and Respiratory 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/T11196","display_name":"Non-Invasive Vital Sign Monitoring","score":0.026799999177455902,"subfield":{"id":"https://openalex.org/subfields/2204","display_name":"Biomedical Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/acute-respiratory-distress-syndrome","display_name":"acute respiratory distress syndrome","score":0.9980000257492065},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9539999961853027},{"id":"https://openalex.org/keywords/feature-ablation","display_name":"feature ablation","score":0.4909999966621399},{"id":"https://openalex.org/keywords/model-interpretability","display_name":"model interpretability","score":0.8240000009536743},{"id":"https://openalex.org/keywords/hypoxemic-respiratory-failure","display_name":"hypoxemic respiratory failure","score":0.7950000166893005},{"id":"https://openalex.org/keywords/random-forest-comparison","display_name":"random forest comparison","score":0.5550000071525574},{"id":"https://openalex.org/keywords/clinical-screening","display_name":"clinical screening","score":0.4860000014305115}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8902263045310974},{"id":"https://openalex.org/C2776348555","wikidata":"https://www.wikidata.org/wiki/Q344873","display_name":"ARDS","level":3,"score":0.6978816390037537},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5721434354782104},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5637502074241638},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5573722124099731},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5473145246505737},{"id":"https://openalex.org/C2909621147","wikidata":"https://www.wikidata.org/wiki/Q344873","display_name":"Acute respiratory distress","level":3,"score":0.541021466255188},{"id":"https://openalex.org/C2778827112","wikidata":"https://www.wikidata.org/wiki/Q22245680","display_name":"Feature engineering","level":3,"score":0.517117977142334},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5031887888908386},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.49557197093963623},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.48493197560310364},{"id":"https://openalex.org/C197424946","wikidata":"https://www.wikidata.org/wiki/Q1165717","display_name":"Waveform","level":3,"score":0.48486241698265076},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.47336098551750183},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.45658156275749207},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.3934746980667114},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.19876176118850708},{"id":"https://openalex.org/C2777714996","wikidata":"https://www.wikidata.org/wiki/Q7886","display_name":"Lung","level":2,"score":0.15654638409614563},{"id":"https://openalex.org/C554190296","wikidata":"https://www.wikidata.org/wiki/Q47528","display_name":"Radar","level":2,"score":0.0},{"id":"https://openalex.org/C76155785","wikidata":"https://www.wikidata.org/wiki/Q418","display_name":"Telecommunications","level":1,"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/C138885662","wikidata":"https://www.wikidata.org/wiki/Q5891","display_name":"Philosophy","level":0,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:arXiv.org:2109.12323","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.12323","pdf_url":"https://arxiv.org/pdf/2109.12323","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"doi:10.48550/arxiv.2109.12323","doi":"https://doi.org/10.48550/arxiv.2109.12323","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2109.12323","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:2109.12323","doi":null,"is_oa":true,"landing_page_url":"http://arxiv.org/abs/2109.12323","pdf_url":"https://arxiv.org/pdf/2109.12323","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[{"score":0.9391000270843506,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"sustainable_development_goals_aurora":[{"score":0.8999999761581421,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":46,"referenced_works":["https://openalex.org/W71813670","https://openalex.org/W1803784511","https://openalex.org/W1891795461","https://openalex.org/W2040607555","https://openalex.org/W2041783300","https://openalex.org/W2054576479","https://openalex.org/W2054929278","https://openalex.org/W2072793207","https://openalex.org/W2120144999","https://openalex.org/W2139054399","https://openalex.org/W2147819048","https://openalex.org/W2163605009","https://openalex.org/W2221898772","https://openalex.org/W2286228001","https://openalex.org/W2322956032","https://openalex.org/W2328140773","https://openalex.org/W2460642893","https://openalex.org/W2510506994","https://openalex.org/W2571548403","https://openalex.org/W2605056515","https://openalex.org/W2737191124","https://openalex.org/W2765466382","https://openalex.org/W2774525674","https://openalex.org/W2790660115","https://openalex.org/W2889242407","https://openalex.org/W2891612330","https://openalex.org/W2897938447","https://openalex.org/W2928542791","https://openalex.org/W2936503027","https://openalex.org/W2945976633","https://openalex.org/W2962858109","https://openalex.org/W2963446712","https://openalex.org/W2963811535","https://openalex.org/W2964121744","https://openalex.org/W2965994363","https://openalex.org/W2968510204","https://openalex.org/W2968594437","https://openalex.org/W2970971581","https://openalex.org/W2992144222","https://openalex.org/W3000630830","https://openalex.org/W3008872739","https://openalex.org/W3011237080","https://openalex.org/W3046296398","https://openalex.org/W3099319456","https://openalex.org/W3101981467","https://openalex.org/W3103145119"],"related_works":["https://openalex.org/W7008668992","https://openalex.org/W4413127315","https://openalex.org/W3155436984","https://openalex.org/W4417197872","https://openalex.org/W3040694126","https://openalex.org/W4393256904","https://openalex.org/W3165008579","https://openalex.org/W2971289064","https://openalex.org/W3124012405","https://openalex.org/W4381137482"],"abstract_inverted_index":{"The":[0],"acute":[1],"respiratory":[2,12],"distress":[3],"syndrome":[4],"(ARDS)":[5],"is":[6,21],"a":[7,32,67],"severe":[8],"form":[9],"of":[10,17,41,133,141,158,167],"hypoxemic":[11],"failure":[13],"with":[14,78],"in-hospital":[15],"mortality":[16,20],"35-46%.":[18],"High":[19],"thought":[22],"to":[23,28,58,123],"be":[24,121],"related":[25],"in":[26,30,37,82,171],"part":[27],"challenges":[29],"making":[31],"prompt":[33],"diagnosis,":[34],"which":[35],"may":[36,56,120,136,154,163],"turn":[38],"delay":[39],"implementation":[40],"evidence-based":[42],"therapies.":[43],"A":[44],"deep":[45],"neural":[46,69],"network":[47],"(DNN)":[48],"algorithm":[49],"utilizing":[50],"unbiased":[51],"ventilator":[52],"waveform":[53,150],"data":[54,173],"(VWD)":[55],"help":[57],"improve":[59,164],"screening":[60],"for":[61,112],"ARDS.":[62],"We":[63],"first":[64],"show":[65],"that":[66,100,119,129],"convolutional":[68],"network-based":[70],"ARDS":[71],"detection":[72],"model":[73,102],"can":[74,103],"outperform":[75],"prior":[76],"work":[77],"random":[79],"forest":[80],"models":[81,143,161],"AUC":[83],"(0.95+/-0.019":[84],"vs.":[85],"0.88+/-0.064),":[86],"accuracy":[87],"(0.84+/-0.026":[88],"vs":[89,94],"0.80+/-0.078),":[90],"and":[91,116,162],"specificity":[92],"(0.81+/-0.06":[93],"0.71+/-0.089).":[95],"Frequency":[96],"ablation":[97],"studies":[98],"imply":[99],"our":[101,177],"learn":[104],"features":[105],"from":[106],"low":[107],"frequency":[108],"domains":[109],"typically":[110],"used":[111],"expert":[113],"feature":[114],"engineering,":[115],"high-frequency":[117,131,169],"information":[118,170],"difficult":[122],"manually":[124],"featurize.":[125],"Further":[126],"experiments":[127],"suggest":[128],"subtle,":[130],"components":[132],"physiologic":[134,149,160,172],"signals":[135],"explain":[137],"the":[138,165,175],"superior":[139],"performance":[140,176],"DL":[142,178],"over":[144],"traditional":[145],"ML":[146],"when":[147],"using":[148],"data.":[151],"Our":[152],"observations":[153],"enable":[155],"improved":[156],"interpretability":[157],"DL-based":[159],"understanding":[166],"how":[168],"impacts":[174],"model.":[179]},"counts_by_year":[],"updated_date":"2026-10-16T09:59:22.235173","created_date":"2021-10-11T00:00:00"}
