{"id":"https://openalex.org/W7214282921","doi":"https://doi.org/10.3390/diagnostics16193118","title":"Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment","display_name":"Explainable Machine Learning for Prediction of Future High-Severity States Using Longitudinal APACHE-II Trajectories: Internal Validation and Cross-Cohort Portability Assessment","publication_year":2026,"publication_date":"2026-09-25","ids":{"openalex":"https://openalex.org/W7214282921","doi":"https://doi.org/10.3390/diagnostics16193118","pmid":"https://pubmed.ncbi.nlm.nih.gov/42882273","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/13664044"},"language":"en","primary_location":{"id":"doi:10.3390/diagnostics16193118","doi":"https://doi.org/10.3390/diagnostics16193118","is_oa":true,"landing_page_url":"https://doi.org/10.3390/diagnostics16193118","pdf_url":null,"source":{"id":"https://openalex.org/S4210172076","display_name":"Diagnostics","issn_l":"2075-4418","issn":["2075-4418"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Diagnostics","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref","doaj","pubmed"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://doi.org/10.3390/diagnostics16193118","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5107737332","display_name":"\u0130rem Akpolat","orcid":"https://orcid.org/0000-0002-6136-0021","observed_orcids":["https://orcid.org/0000-0002-6136-0021"]},"institutions":[{"id":"https://openalex.org/I96268669","display_name":"Okan University","ror":"https://ror.org/054d5vq03","country_code":"TR","type":"education","lineage":["https://openalex.org/I96268669"]}],"countries":["TR"],"is_corresponding":true,"raw_author_name":"\u0130rem Akpolat","raw_affiliation_strings":["Department of Nutrition and Dietetics, \u0130stanbul Okan University, Istanbul 34959, T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Nutrition and Dietetics, \u0130stanbul Okan University, Istanbul 34959, T\u00fcrkiye","institution_ids":["https://openalex.org/I96268669"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087083279","display_name":"Fatma \u00c7elik","orcid":"https://orcid.org/0000-0003-0192-0151","observed_orcids":["https://orcid.org/0000-0003-0192-0151"]},"institutions":[{"id":"https://openalex.org/I4210104933","display_name":"Biruni University","ror":"https://ror.org/01nkhmn89","country_code":"TR","type":"education","lineage":["https://openalex.org/I4210104933"]}],"countries":["TR"],"is_corresponding":false,"raw_author_name":"Fatma \u00c7elik","raw_affiliation_strings":["Department of Nutrition and Dietetics, Faculty of Health Sciences, Biruni University, Istanbul 34015, T\u00fcrkiye"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Nutrition and Dietetics, Faculty of Health Sciences, Biruni University, Istanbul 34015, T\u00fcrkiye","institution_ids":["https://openalex.org/I4210104933"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5107737332"],"corresponding_institution_ids":["https://openalex.org/I96268669"],"apc_list":{"value":2600,"currency":"CHF","value_usd":3137},"apc_paid":{"value":2600,"currency":"CHF","value_usd":3137},"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.0,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"16","issue":"19","first_page":"3118","last_page":"3118"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.4966000020503998,"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":0.4966000020503998,"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/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.47510001063346863,"subfield":{"id":"https://openalex.org/subfields/2706","display_name":"Critical Care and Intensive Care 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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.007199999876320362,"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/intensive-care-unit-patients","display_name":"intensive care unit patients","score":0.8209999799728394},{"id":"https://openalex.org/keywords/total-parenteral-nutrition","display_name":"total parenteral nutrition","score":0.9110000133514404},{"id":"https://openalex.org/keywords/catboost","display_name":"CatBoost","score":0.7419999837875366},{"id":"https://openalex.org/keywords/shap","display_name":"SHAP","score":0.7459999918937683},{"id":"https://openalex.org/keywords/eicu","display_name":"eICU","score":0.718999981880188},{"id":"https://openalex.org/keywords/risk-stratification","display_name":"risk stratification","score":0.7910000085830688},{"id":"https://openalex.org/keywords/model-calibration","display_name":"model calibration","score":0.4880000054836273},{"id":"https://openalex.org/keywords/nested-cross-validation","display_name":"nested cross-validation","score":0.6269999742507935},{"id":"https://openalex.org/keywords/internal-validation","display_name":"internal validation","score":0.5099999904632568},{"id":"https://openalex.org/keywords/feature-engineering","display_name":"feature engineering","score":0.3490000069141388},{"id":"https://openalex.org/keywords/xgboost","display_name":"XGBoost","score":0.42100000381469727}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7009999752044678},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6437000036239624},{"id":"https://openalex.org/C63000827","wikidata":"https://www.wikidata.org/wiki/Q3080428","display_name":"Software portability","level":2,"score":0.5597000122070312},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5148000121116638},{"id":"https://openalex.org/C165838908","wikidata":"https://www.wikidata.org/wiki/Q736777","display_name":"Calibration","level":2,"score":0.4839000105857849},{"id":"https://openalex.org/C12725497","wikidata":"https://www.wikidata.org/wiki/Q810247","display_name":"Baseline (sea)","level":2,"score":0.46970000863075256},{"id":"https://openalex.org/C2777895361","wikidata":"https://www.wikidata.org/wiki/Q1758614","display_name":"Longitudinal study","level":2,"score":0.45089998841285706},{"id":"https://openalex.org/C2776401178","wikidata":"https://www.wikidata.org/wiki/Q12050496","display_name":"Feature (linguistics)","level":2,"score":0.3562000095844269},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.32919999957084656},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.31439998745918274},{"id":"https://openalex.org/C2779466056","wikidata":"https://www.wikidata.org/wiki/Q107630651","display_name":"Time point","level":2,"score":0.3052999973297119},{"id":"https://openalex.org/C35405484","wikidata":"https://www.wikidata.org/wiki/Q4967066","display_name":"Brier score","level":2,"score":0.2985000014305115},{"id":"https://openalex.org/C173633133","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw score","level":3,"score":0.2953999936580658},{"id":"https://openalex.org/C27403532","wikidata":"https://www.wikidata.org/wiki/Q5532474","display_name":"Generalized estimating equation","level":2,"score":0.2937000095844269},{"id":"https://openalex.org/C2987404301","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.28850001096725464},{"id":"https://openalex.org/C148483581","wikidata":"https://www.wikidata.org/wiki/Q446488","display_name":"Feature selection","level":2,"score":0.2815000116825104},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.2806999981403351},{"id":"https://openalex.org/C132964779","wikidata":"https://www.wikidata.org/wiki/Q2110223","display_name":"Raw data","level":2,"score":0.2773999869823456},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2721000015735626},{"id":"https://openalex.org/C45804977","wikidata":"https://www.wikidata.org/wiki/Q7239673","display_name":"Predictive modelling","level":2,"score":0.26600000262260437},{"id":"https://openalex.org/C72563966","wikidata":"https://www.wikidata.org/wiki/Q1303415","display_name":"Cohort","level":2,"score":0.26579999923706055},{"id":"https://openalex.org/C3020672099","wikidata":"https://www.wikidata.org/wiki/Q857354","display_name":"Longitudinal data","level":2,"score":0.2630999982357025},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.26089999079704285},{"id":"https://openalex.org/C201903717","wikidata":"https://www.wikidata.org/wiki/Q1778788","display_name":"Cohort study","level":2,"score":0.2597000002861023},{"id":"https://openalex.org/C203092338","wikidata":"https://www.wikidata.org/wiki/Q1340863","display_name":"Clinical endpoint","level":3,"score":0.2587999999523163}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.3390/diagnostics16193118","doi":"https://doi.org/10.3390/diagnostics16193118","is_oa":true,"landing_page_url":"https://doi.org/10.3390/diagnostics16193118","pdf_url":null,"source":{"id":"https://openalex.org/S4210172076","display_name":"Diagnostics","issn_l":"2075-4418","issn":["2075-4418"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Diagnostics","raw_type":"journal-article"},{"id":"pmh:oai:pubmedcentral.nih.gov:13664044","doi":null,"is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13664044/","pdf_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13664044/pdf/diagnostics-16-03118.pdf","source":{"id":"https://openalex.org/S2764455111","display_name":"PubMed Central","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Diagnostics (Basel)","raw_type":"Text"},{"id":"pmid:42882273","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/42882273","pdf_url":null,"source":{"id":"https://openalex.org/S4306525036","display_name":"PubMed","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I1299303238","host_organization_name":"National Institutes of Health","host_organization_lineage":["https://openalex.org/I1299303238"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Diagnostics (Basel, Switzerland)","raw_type":"Journal Article"},{"id":"pmh:oai:doaj.org/article:4f8e3749de4b45c094e3d109e95625cc","doi":null,"is_oa":false,"landing_page_url":"https://doaj.org/article/4f8e3749de4b45c094e3d109e95625cc","pdf_url":null,"source":{"id":"https://openalex.org/S4306401280","display_name":"DOAJ (DOAJ: Directory of Open Access Journals)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"Diagnostics, Vol 16, Iss 19, p 3118 (2026)","raw_type":"article"}],"best_oa_location":{"id":"doi:10.3390/diagnostics16193118","doi":"https://doi.org/10.3390/diagnostics16193118","is_oa":true,"landing_page_url":"https://doi.org/10.3390/diagnostics16193118","pdf_url":null,"source":{"id":"https://openalex.org/S4210172076","display_name":"Diagnostics","issn_l":"2075-4418","issn":["2075-4418"],"is_oa":true,"is_in_doaj":true,"is_core":true,"listed_in":["cwts-core","doaj","norway-1","poland-70","russia-white-list-1","vabb-shw"],"host_organization":"https://openalex.org/P4310310987","host_organization_name":"Multidisciplinary Digital Publishing Institute","host_organization_lineage":["https://openalex.org/P4310310987"],"host_organization_lineage_names":["Multidisciplinary Digital Publishing Institute"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Diagnostics","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":0.9763000011444092,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[{"display_name":"Observational Study","id":"https://openalex.org/study-designs/observational-study"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320323388","display_name":"Dicle \u00dcniversitesi","ror":"https://ror.org/0257dtg16"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W7214282921.pdf","grobid_xml":"https://content.openalex.org/works/W7214282921.grobid-xml"},"referenced_works_count":51,"referenced_works":["https://openalex.org/W788217788","https://openalex.org/W1535879378","https://openalex.org/W1582743834","https://openalex.org/W1898928487","https://openalex.org/W1992745512","https://openalex.org/W2000566182","https://openalex.org/W2050845046","https://openalex.org/W2056132907","https://openalex.org/W2064675550","https://openalex.org/W2072492033","https://openalex.org/W2115200234","https://openalex.org/W2128349740","https://openalex.org/W2157331557","https://openalex.org/W2166190627","https://openalex.org/W2169167455","https://openalex.org/W2169685811","https://openalex.org/W2282821441","https://openalex.org/W2295598076","https://openalex.org/W2396881363","https://openalex.org/W2498119267","https://openalex.org/W2625625371","https://openalex.org/W2782135811","https://openalex.org/W2784499877","https://openalex.org/W2787810682","https://openalex.org/W2806031239","https://openalex.org/W2891400669","https://openalex.org/W2911964244","https://openalex.org/W2927351257","https://openalex.org/W2934399013","https://openalex.org/W2937186836","https://openalex.org/W2945976633","https://openalex.org/W2982580298","https://openalex.org/W2999615587","https://openalex.org/W3011812967","https://openalex.org/W3013681994","https://openalex.org/W3017637887","https://openalex.org/W3106297998","https://openalex.org/W3111842226","https://openalex.org/W3119527628","https://openalex.org/W3190773357","https://openalex.org/W4210531228","https://openalex.org/W4224316420","https://openalex.org/W4225326659","https://openalex.org/W4285020063","https://openalex.org/W4293242440","https://openalex.org/W4313439128","https://openalex.org/W4384297410","https://openalex.org/W4392811590","https://openalex.org/W4399775722","https://openalex.org/W4405584448","https://openalex.org/W4411006812"],"related_works":["https://openalex.org/W4221044738","https://openalex.org/W3190773357","https://openalex.org/W4241541025","https://openalex.org/W3011812967","https://openalex.org/W4408090682","https://openalex.org/W4399583797","https://openalex.org/W2898824759","https://openalex.org/W7208784086","https://openalex.org/W4229025171","https://openalex.org/W3017410673"],"abstract_inverted_index":{"Background:":[0],"Longitudinal":[1],"severity":[2],"assessment":[3,67,253],"may":[4],"provide":[5],"more":[6],"informative":[7],"risk":[8],"stratification":[9],"than":[10,377],"reliance":[11],"on":[12],"a":[13,33,176,200,235,242,345],"single":[14],"admission":[15,106],"score":[16,60,220],"in":[17,42,174,350,391],"intensive":[18,43],"care.":[19],"This":[20],"study":[21],"developed":[22],"and":[23,123,139,159,168,205,231,241,266,355,359,366,381,410,418],"internally":[24],"validated":[25],"an":[26,58,169,206],"explainable":[27],"machine":[28,130],"learning":[29,131],"framework":[30],"for":[31,184,405],"predicting":[32],"subsequent":[34,346],"APACHE-II-defined":[35,347],"high-severity":[36,98,348],"state":[37,349],"using":[38,135,269,415],"repeated":[39,337],"APACHE-II":[40,59,109,147,166,192,252,271,305,331,338,370,403],"assessments":[41,339],"care":[44],"unit":[45],"(ICU)":[46],"patients":[47,88],"receiving":[48],"total":[49],"parenteral":[50],"nutrition":[51],"(TPN).":[52],"The":[53,190,250],"endpoint":[54,171,367],"was":[55,72,89,181,217,287],"defined":[56],"as":[57],"of":[61,84,91,203,210,239,246,336,344,401],"\u2265":[62],"20":[63],"at":[64,77],"the":[65,96,112,196,277,329,364,369,382,397],"final":[66],"(T10),":[68],"while":[69],"predictor":[70],"information":[71,110],"restricted":[73],"to":[74,309],"measurements":[75,167],"obtained":[76],"T0\u2013T9.":[78],"Methods:":[79],"A":[80],"retrospective":[81],"institutional":[82],"cohort":[83,354],"844":[85],"adult":[86],"ICU":[87,353],"analyzed,":[90],"whom":[92],"214":[93],"(25.4%)":[94],"met":[95],"predefined":[97],"endpoint.":[99],"Four":[100],"principal":[101],"feature":[102],"representations":[103],"were":[104,133,172,374],"evaluated:":[105],"APACHE-II,":[107],"longitudinal":[108,120,165,191,279,330,361,402],"combining":[111],"raw":[113],"T0\u2013T9":[114],"sequence":[115],"with":[116,199,312],"derived":[117],"trajectory":[118],"descriptors,":[119],"laboratory":[121,319],"information,":[122],"multimodal":[124],"combinations":[125],"including":[126],"baseline":[127,261,322],"characteristics.":[128],"Six":[129],"algorithms":[132],"compared":[134],"stratified":[136],"five-fold":[137],"cross-validation":[138,233],"independent":[140,207,419],"hold-out":[141,208],"testing.":[142],"Additional":[143],"analyses":[144],"included":[145],"simpler":[146,360],"comparators,":[148],"sequential":[149,375],"observation":[150,372],"truncation,":[151],"nested":[152,232],"cross-validation,":[153],"calibration":[154,216,223],"assessment,":[155],"threshold":[156],"sensitivity":[157],"analysis,":[158],"SHAP-based":[160],"model":[161,180,194,310,384],"interpretation.":[162],"Because":[163],"equivalent":[164,170],"unavailable":[173],"eICU,":[175],"separate":[177],"harmonized":[178],"XGBoost":[179],"used":[182],"only":[183],"exploratory":[185],"cross-cohort":[186],"portability":[187],"assessment.":[188],"Results:":[189],"CatBoost":[193,280],"achieved":[195],"highest":[197],"performance,":[198],"cross-validated":[201],"ROC-AUC":[202,209,238,245,256],"0.924":[204],"0.921":[211],"(95%":[212],"CI:":[213],"0.878\u20130.960).":[214],"Hold-out":[215],"good":[218],"(Brier":[219],"=":[221,225,229,257],"0.095,":[222],"intercept":[224],"\u2212":[226],"0.03,":[227],"slope":[228,264],"0.98),":[230],"yielded":[234],"pooled":[236],"out-of-fold":[237],"0.908":[240],"mean":[243],"outer-fold":[244],"0.920":[247],"\u00b1":[248],"0.038.":[249],"latest":[251],"alone":[254],"(T9;":[255],"0.553),":[258],"change":[259],"from":[260],"(0.576),":[262],"ordinal":[263],"(0.539),":[265],"logistic":[267],"regression":[268],"engineered":[270],"descriptors":[272,314],"(0.633)":[273],"performed":[274],"substantially":[275,356],"below":[276],"full":[278],"model.":[281],"Sequential":[282],"truncation":[283],"showed":[284,302],"that":[285,303],"discrimination":[286,327,343],"retained":[288],"after":[289],"removing":[290],"later":[291],"observations,":[292],"although":[293],"performance":[294],"varied":[295],"non-monotonically":[296],"across":[297],"truncated":[298],"sequences.":[299],"SHAP":[300],"analysis":[301],"multiple":[304],"observations":[306],"contributed":[307],"prominently":[308],"predictions,":[311],"trajectory-derived":[313],"providing":[315],"complementary":[316],"contributions.":[317],"Adding":[318],"trajectories":[320],"or":[321],"characteristics":[323],"did":[324],"not":[325,386],"improve":[326],"over":[328],"representation.":[332],"Conclusions:":[333],"Nonlinear":[334],"modeling":[335,404],"provided":[340],"strong":[341],"internal":[342,398],"this":[351,406],"TPN-treated":[352],"outperformed":[357],"single-score":[358],"comparators.":[362],"However,":[363],"predictors":[365],"share":[368],"construct,":[371],"indices":[373],"rather":[376],"standardized":[378,416],"clock-time":[379],"intervals,":[380],"primary":[383],"could":[385],"undergo":[387],"conventional":[388],"external":[389],"validation":[390],"eICU.":[392],"These":[393],"findings":[394],"therefore":[395],"support":[396],"predictive":[399],"value":[400],"specific":[407],"severity-state":[408],"task":[409],"warrant":[411],"prospective":[412],"multicenter":[413],"evaluation":[414],"timing":[417],"clinical":[420],"outcomes.":[421]},"counts_by_year":[],"updated_date":"2026-11-10T11:39:43.021295","created_date":"2026-09-26T00:00:00"}