{"id":"https://openalex.org/W3138309625","doi":"https://doi.org/10.1109/ichi48887.2020.9374377","title":"Multimodal Early Septic Shock Prediction Model using Lasso Regression with Decaying Response","display_name":"Multimodal Early Septic Shock Prediction Model using Lasso Regression with Decaying Response","publication_year":2020,"publication_date":"2020-11-01","ids":{"openalex":"https://openalex.org/W3138309625","doi":"https://doi.org/10.1109/ichi48887.2020.9374377","mag":"3138309625"},"language":"en","primary_location":{"id":"doi:10.1109/ichi48887.2020.9374377","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichi48887.2020.9374377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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/A5040821857","display_name":"Ibrahim Hammoud","orcid":null},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Ibrahim Hammoud","raw_affiliation_strings":["Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5005648942","display_name":"I. V. Ramakrishnan","orcid":"https://orcid.org/0000-0002-1768-7043"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"IV Ramakrishnan","raw_affiliation_strings":["Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook, NY, USA","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5112072920","display_name":"Mark C. Henry","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Mark Henry","raw_affiliation_strings":["Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook, NY, USA","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5112619583","display_name":"Eric J. Morley","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Eric Morley","raw_affiliation_strings":["Stony Brook, NY, USA"],"affiliations":[{"raw_affiliation_string":"Stony Brook, NY, USA","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5040821857"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2894,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.62482926,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"3"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2713","display_name":"Epidemiology"},"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/T13702","display_name":"Machine Learning in Healthcare","score":0.991599977016449,"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9835000038146973,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/septic-shock","display_name":"Septic shock","score":0.8601644039154053},{"id":"https://openalex.org/keywords/lasso","display_name":"Lasso (programming language)","score":0.6175063252449036},{"id":"https://openalex.org/keywords/receiver-operating-characteristic","display_name":"Receiver operating characteristic","score":0.5723645091056824},{"id":"https://openalex.org/keywords/shock","display_name":"Shock (circulatory)","score":0.5661605000495911},{"id":"https://openalex.org/keywords/model-selection","display_name":"Model selection","score":0.5408161282539368},{"id":"https://openalex.org/keywords/sepsis","display_name":"Sepsis","score":0.5031651854515076},{"id":"https://openalex.org/keywords/selection","display_name":"Selection (genetic algorithm)","score":0.46199607849121094},{"id":"https://openalex.org/keywords/regression","display_name":"Regression","score":0.4531269073486328},{"id":"https://openalex.org/keywords/regression-analysis","display_name":"Regression analysis","score":0.4415639042854309},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.43018826842308044},{"id":"https://openalex.org/keywords/event","display_name":"Event (particle physics)","score":0.42321357131004333},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.37301158905029297},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.36565643548965454},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3248426914215088},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.24741041660308838},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.1410847306251526}],"concepts":[{"id":"https://openalex.org/C2777628635","wikidata":"https://www.wikidata.org/wiki/Q1765564","display_name":"Septic shock","level":3,"score":0.8601644039154053},{"id":"https://openalex.org/C37616216","wikidata":"https://www.wikidata.org/wiki/Q3218363","display_name":"Lasso (programming language)","level":2,"score":0.6175063252449036},{"id":"https://openalex.org/C58471807","wikidata":"https://www.wikidata.org/wiki/Q327120","display_name":"Receiver operating characteristic","level":2,"score":0.5723645091056824},{"id":"https://openalex.org/C2781300812","wikidata":"https://www.wikidata.org/wiki/Q178061","display_name":"Shock (circulatory)","level":2,"score":0.5661605000495911},{"id":"https://openalex.org/C93959086","wikidata":"https://www.wikidata.org/wiki/Q6888345","display_name":"Model selection","level":2,"score":0.5408161282539368},{"id":"https://openalex.org/C2778384902","wikidata":"https://www.wikidata.org/wiki/Q183134","display_name":"Sepsis","level":2,"score":0.5031651854515076},{"id":"https://openalex.org/C81917197","wikidata":"https://www.wikidata.org/wiki/Q628760","display_name":"Selection (genetic algorithm)","level":2,"score":0.46199607849121094},{"id":"https://openalex.org/C83546350","wikidata":"https://www.wikidata.org/wiki/Q1139051","display_name":"Regression","level":2,"score":0.4531269073486328},{"id":"https://openalex.org/C152877465","wikidata":"https://www.wikidata.org/wiki/Q208042","display_name":"Regression analysis","level":2,"score":0.4415639042854309},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.43018826842308044},{"id":"https://openalex.org/C2779662365","wikidata":"https://www.wikidata.org/wiki/Q5416694","display_name":"Event (particle physics)","level":2,"score":0.42321357131004333},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.37301158905029297},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.36565643548965454},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3248426914215088},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.24741041660308838},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.1410847306251526},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.0},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ichi48887.2020.9374377","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichi48887.2020.9374377","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 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":23,"referenced_works":["https://openalex.org/W1900911852","https://openalex.org/W1943063538","https://openalex.org/W2019582965","https://openalex.org/W2046788142","https://openalex.org/W2055092429","https://openalex.org/W2101082552","https://openalex.org/W2160691650","https://openalex.org/W2280404143","https://openalex.org/W2315642910","https://openalex.org/W2522105146","https://openalex.org/W2609231317","https://openalex.org/W2776803885","https://openalex.org/W2883553986","https://openalex.org/W2883762974","https://openalex.org/W2932423054","https://openalex.org/W2963704753","https://openalex.org/W2999219728","https://openalex.org/W3162617641","https://openalex.org/W3183325496","https://openalex.org/W4244772879","https://openalex.org/W4297685524","https://openalex.org/W6742557613","https://openalex.org/W7073705271"],"related_works":["https://openalex.org/W4385649027","https://openalex.org/W4256576576","https://openalex.org/W2409647306","https://openalex.org/W4400094315","https://openalex.org/W3111987831","https://openalex.org/W3090384609","https://openalex.org/W1515021623","https://openalex.org/W3155171010","https://openalex.org/W2119862467","https://openalex.org/W3015383640"],"abstract_inverted_index":{"Sepsis":[0],"is":[1,24],"one":[2],"of":[3,7,52,68,74,82,114],"the":[4,72,75,96,106,112,162,170,193],"leading":[5],"causes":[6],"in-hospital":[8],"deaths.":[9],"In":[10],"this":[11],"paper,":[12],"we":[13,99,168],"propose":[14,179],"a":[15,25,64,80,156,204],"multimodal":[16],"early":[17,125],"prediction":[18,153],"system":[19,33],"for":[20,28,161,183,203],"septic":[21,53,76,107,128],"shock,":[22],"which":[23],"serious":[26],"complication":[27],"patients":[29,35,49],"with":[30,44,90],"sepsis.":[31],"Our":[32,57,84,133],"utilizes":[34],"vital":[36],"and":[37,63,119,127,141,173,178],"laboratory":[38],"time":[39,67,104,144,154,176],"series":[40],"data":[41],"in":[42,55,103,130,137],"combination":[43],"medical":[45],"notes":[46],"to":[47,94,117,123,201],"predict":[48,124],"at":[50,79,145,155],"risk":[51,97],"shock":[54,77,108,129],"real-time.":[56],"model":[58,95,116,134,184,194],"achieves":[59],"0.89":[60],"ROC":[61,139,171],"AUC":[62,140,172],"median":[65,142,174],"detection":[66,143,175],"30.64":[69],"hours":[70],"before":[71],"onset":[73],"event":[78],"specificity":[81],"0.67.":[83],"proposed":[85],"method":[86],"uses":[87],"lasso":[88],"regression":[89],"modified":[91,200],"response":[92],"decay":[93],"as":[98],"go":[100],"further":[101],"away":[102],"from":[105],"event.":[109],"We":[110,189],"compare":[111],"performance":[113],"our":[115],"baseline":[118],"state-of-the-art":[120],"models":[121],"used":[122],"deterioration":[126],"ICU":[131],"patients.":[132],"shows":[135],"improvement":[136],"both":[138],"all":[146],"specificities.":[147],"This":[148],"translates":[149],"into":[150],"an":[151,180],"earlier":[152],"higher":[157],"true":[158],"positive":[159,165],"rate":[160],"same":[163],"false":[164],"rate.":[166],"Moreover,":[167],"investigate":[169],"trade-off":[177],"automated":[181],"way":[182],"selection":[185,195],"under":[186],"such":[187],"trade-off.":[188],"also":[190,198],"demonstrate":[191],"how":[192],"step":[196],"could":[197],"be":[199],"optimize":[202],"predefined":[205],"user":[206],"utility.":[207]},"counts_by_year":[{"year":2025,"cited_by_count":4},{"year":2022,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
