{"id":"https://openalex.org/W4408520993","doi":"https://doi.org/10.1109/bhi62660.2024.10913552","title":"Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss","display_name":"Non-invasive Electrolyte Estimation Using Multi-lead ECG Data via Semi-Supervised Contrastive Learning with an Adaptive Loss","publication_year":2024,"publication_date":"2024-11-10","ids":{"openalex":"https://openalex.org/W4408520993","doi":"https://doi.org/10.1109/bhi62660.2024.10913552"},"language":"en","primary_location":{"id":"doi:10.1109/bhi62660.2024.10913552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi62660.2024.10913552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)","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/A5073815583","display_name":"Zhale Nowroozilarki","orcid":"https://orcid.org/0000-0002-9608-8312"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Zhale Nowroozilarki","raw_affiliation_strings":["Texas A&M University,Department of Computer Science & Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&M University,Department of Computer Science & Engineering","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101698050","display_name":"Sicong Huang","orcid":"https://orcid.org/0009-0009-3596-5244"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sicong Huang","raw_affiliation_strings":["Texas A&M University,Department of Computer Science & Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&M University,Department of Computer Science & Engineering","institution_ids":["https://openalex.org/I91045830"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5017974764","display_name":"Rohan Khera","orcid":"https://orcid.org/0000-0001-9467-6199"},"institutions":[{"id":"https://openalex.org/I32971472","display_name":"Yale University","ror":"https://ror.org/03v76x132","country_code":"US","type":"education","lineage":["https://openalex.org/I32971472"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Rohan Khera","raw_affiliation_strings":["Yale School of Medicine,Department of Internal Medicine"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Yale School of Medicine,Department of Internal Medicine","institution_ids":["https://openalex.org/I32971472"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040096171","display_name":"Bobak J. Mortazavi","orcid":"https://orcid.org/0000-0002-2655-2095"},"institutions":[{"id":"https://openalex.org/I91045830","display_name":"Texas A&M University","ror":"https://ror.org/01f5ytq51","country_code":"US","type":"education","lineage":["https://openalex.org/I91045830"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bobak J. Mortazavi","raw_affiliation_strings":["Texas A&M University,Department of Computer Science & Engineering"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Texas A&M University,Department of Computer Science & Engineering","institution_ids":["https://openalex.org/I91045830"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2913,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.62018309,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":91,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"8"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10876","display_name":"Fault Detection and Control Systems","score":0.9865000247955322,"subfield":{"id":"https://openalex.org/subfields/2207","display_name":"Control and Systems Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11472","display_name":"Analytical Chemistry and Sensors","score":0.958299994468689,"subfield":{"id":"https://openalex.org/subfields/1502","display_name":"Bioengineering"},"field":{"id":"https://openalex.org/fields/15","display_name":"Chemical Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11667","display_name":"Advanced Chemical Sensor Technologies","score":0.9336000084877014,"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/computer-science","display_name":"Computer science","score":0.6764823794364929},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5685989856719971},{"id":"https://openalex.org/keywords/lead","display_name":"Lead (geology)","score":0.45238494873046875},{"id":"https://openalex.org/keywords/estimation","display_name":"Estimation","score":0.4317753314971924},{"id":"https://openalex.org/keywords/pattern-recognition","display_name":"Pattern recognition (psychology)","score":0.4120991826057434},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.38344311714172363},{"id":"https://openalex.org/keywords/speech-recognition","display_name":"Speech recognition","score":0.3702574372291565},{"id":"https://openalex.org/keywords/engineering","display_name":"Engineering","score":0.10009545087814331}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6764823794364929},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5685989856719971},{"id":"https://openalex.org/C2777093003","wikidata":"https://www.wikidata.org/wiki/Q6508345","display_name":"Lead (geology)","level":2,"score":0.45238494873046875},{"id":"https://openalex.org/C96250715","wikidata":"https://www.wikidata.org/wiki/Q965330","display_name":"Estimation","level":2,"score":0.4317753314971924},{"id":"https://openalex.org/C153180895","wikidata":"https://www.wikidata.org/wiki/Q7148389","display_name":"Pattern recognition (psychology)","level":2,"score":0.4120991826057434},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.38344311714172363},{"id":"https://openalex.org/C28490314","wikidata":"https://www.wikidata.org/wiki/Q189436","display_name":"Speech recognition","level":1,"score":0.3702574372291565},{"id":"https://openalex.org/C127413603","wikidata":"https://www.wikidata.org/wiki/Q11023","display_name":"Engineering","level":0,"score":0.10009545087814331},{"id":"https://openalex.org/C201995342","wikidata":"https://www.wikidata.org/wiki/Q682496","display_name":"Systems engineering","level":1,"score":0.0},{"id":"https://openalex.org/C114793014","wikidata":"https://www.wikidata.org/wiki/Q52109","display_name":"Geomorphology","level":1,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bhi62660.2024.10913552","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bhi62660.2024.10913552","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2024 IEEE EMBS International Conference on Biomedical and Health Informatics (BHI)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.41999998688697815,"display_name":"Climate action","id":"https://metadata.un.org/sdg/13"}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":37,"referenced_works":["https://openalex.org/W2024537170","https://openalex.org/W2051880893","https://openalex.org/W2095804932","https://openalex.org/W2102922023","https://openalex.org/W2194775991","https://openalex.org/W2294867897","https://openalex.org/W2346582463","https://openalex.org/W2561176416","https://openalex.org/W2622250050","https://openalex.org/W2913789442","https://openalex.org/W2928542791","https://openalex.org/W2954996726","https://openalex.org/W2974891745","https://openalex.org/W2987704958","https://openalex.org/W3009117699","https://openalex.org/W3012997976","https://openalex.org/W3016214272","https://openalex.org/W3023569947","https://openalex.org/W3033523981","https://openalex.org/W3087846344","https://openalex.org/W3102064161","https://openalex.org/W3110686801","https://openalex.org/W3136766298","https://openalex.org/W3140211173","https://openalex.org/W3198172651","https://openalex.org/W4205351422","https://openalex.org/W4210862231","https://openalex.org/W4212964804","https://openalex.org/W4224018057","https://openalex.org/W4243444138","https://openalex.org/W4291023040","https://openalex.org/W4377011715","https://openalex.org/W4383892171","https://openalex.org/W6774314701","https://openalex.org/W6785972966","https://openalex.org/W6841079142","https://openalex.org/W6856605424"],"related_works":["https://openalex.org/W4255463199","https://openalex.org/W4281691423","https://openalex.org/W2411039299","https://openalex.org/W1856410221","https://openalex.org/W2318949977","https://openalex.org/W2334139353","https://openalex.org/W4251174968","https://openalex.org/W4243014959","https://openalex.org/W2415764305","https://openalex.org/W4254613272"],"abstract_inverted_index":{"Abnormal":[0],"electrolyte":[1,39,47,66,84,177],"concentration":[2],"in":[3,38,74,89,142,150,171],"blood":[4,15,154],"can":[5,34],"lead":[6],"to":[7,60,126],"serious":[8],"conditions":[9],"such":[10,49],"as":[11,50],"renal":[12],"failure,":[13],"high":[14],"pressure,":[16],"and":[17,33,129,148,157,163],"a":[18,26,58,71,96,117,143],"life-threatening":[19],"type":[20],"of":[21,29,43,145,152,166],"arrhythmia.":[22],"Electrocardiogram":[23],"(ECG)":[24],"is":[25,57,70],"non-invasive":[27],"measure":[28],"cardiac":[30],"electrical":[31],"activity":[32],"capture":[35],"subtle":[36],"changes":[37],"changes.":[40],"While":[41],"classification":[42,178],"ECGs":[44,63],"with":[45,98,116,121,137],"abnormal":[46,176],"ranges":[48],"potassium":[51],"levels":[52],"has":[53],"been":[54],"reported,":[55],"there":[56],"need":[59],"leverage":[61],"continuous":[62],"for":[64,79,108,174],"regressing":[65],"values.":[67],"Label":[68],"scarcity":[69],"major":[72],"limitation":[73],"training":[75],"machine":[76],"learning-based":[77],"models":[78],"this":[80],"purpose.":[81],"Additionally,":[82],"measured":[83],"values":[85],"that":[86,133],"are":[87,106],"accessible":[88],"electronic":[90],"health":[91],"records":[92],"datasets":[93,125],"typically":[94],"exhibit":[95],"distribution":[97],"fewer":[99],"data":[100],"points":[101],"at":[102],"the":[103,113,153,172,175,183],"extremes":[104],"which":[105],"crucial":[107],"accurate":[109],"prediction.":[110],"We":[111],"tackle":[112],"above":[114],"challenges":[115],"pretrained":[118],"ECG":[119],"encoder":[120],"large":[122],"but":[123],"unlabeled":[124],"maximize":[127],"generalization":[128],"an":[130,164],"adaptive":[131],"loss":[132],"inversely":[134],"regularizes":[135],"backpropagation":[136],"label":[138],"frequency.":[139],"This":[140],"results":[141],"reduction":[144],"0.1,":[146],"0.16,":[147],"0.41":[149],"MAE":[151],"potassium,":[155],"calcium,":[156],"sodium":[158],"level":[159],"regression":[160],"tasks":[161,179],"respectively,":[162],"improvement":[165,170],"more":[167],"than":[168],"0.15":[169],"AUROC":[173],"when":[180],"compared":[181],"against":[182],"state-of-the-art":[184],"models.":[185]},"counts_by_year":[{"year":2025,"cited_by_count":1}],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}