{"id":"https://openalex.org/W7161722469","doi":"https://doi.org/10.48550/arxiv.2605.18701","title":"Learning Normal Representations for Blood Biomarkers","display_name":"Learning Normal Representations for Blood Biomarkers","publication_year":2026,"publication_date":"2026-05-18","ids":{"openalex":"https://openalex.org/W7161722469","doi":"https://doi.org/10.48550/arxiv.2605.18701"},"language":"en","primary_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13229086","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13229086/","pdf_url":null,"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,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13229086/","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5110391812","display_name":"A Shah","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Shah, Aashna P.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033336429","display_name":"M. Li","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Li, Michelle M.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136494119","display_name":"Yash Lal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Lal, Yash","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033589584","display_name":"Seffi Cohen","orcid":"https://orcid.org/0000-0002-1135-0079"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Cohen, Seffi","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136485194","display_name":"Liat F. Antwarg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Antwarg, Liat F.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5079489460","display_name":"Morgan Sanchez","orcid":"https://orcid.org/0000-0002-6641-4519"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sanchez, Morgan","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5018424095","display_name":"James A. Diao","orcid":"https://orcid.org/0000-0002-6134-4339"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Diao, James A.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136494104","display_name":"Chirag J. Patel","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Patel, Chirag J.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136457354","display_name":"Ben Y. Reis","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reis, Ben Y.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5136454720","display_name":"Ran D. Balicer","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Balicer, Ran D.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5057364990","display_name":"Noa Dagan","orcid":"https://orcid.org/0000-0001-8811-7825"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Dagan, Noa","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5136059686","display_name":"Arjun K. Manrai","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Manrai, Arjun K.","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"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/T10218","display_name":"Sepsis Diagnosis and Treatment","score":0.30809998512268066,"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":0.30809998512268066,"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":0.1412000060081482,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.09210000187158585,"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"}}],"keywords":[{"id":"https://openalex.org/keywords/overfitting","display_name":"Overfitting","score":0.6399999856948853},{"id":"https://openalex.org/keywords/leverage","display_name":"Leverage (statistics)","score":0.5724999904632568},{"id":"https://openalex.org/keywords/subclinical-infection","display_name":"Subclinical infection","score":0.5626000165939331},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.5188999772071838},{"id":"https://openalex.org/keywords/personalized-medicine","display_name":"Personalized medicine","score":0.5031999945640564},{"id":"https://openalex.org/keywords/precision-medicine","display_name":"Precision medicine","score":0.4869999885559082},{"id":"https://openalex.org/keywords/kidney-disease","display_name":"Kidney disease","score":0.43959999084472656},{"id":"https://openalex.org/keywords/biomarker","display_name":"Biomarker","score":0.4284000098705292},{"id":"https://openalex.org/keywords/prior-probability","display_name":"Prior probability","score":0.42309999465942383}],"concepts":[{"id":"https://openalex.org/C22019652","wikidata":"https://www.wikidata.org/wiki/Q331309","display_name":"Overfitting","level":3,"score":0.6399999856948853},{"id":"https://openalex.org/C153083717","wikidata":"https://www.wikidata.org/wiki/Q6535263","display_name":"Leverage (statistics)","level":2,"score":0.5724999904632568},{"id":"https://openalex.org/C113280763","wikidata":"https://www.wikidata.org/wiki/Q2326546","display_name":"Subclinical infection","level":2,"score":0.5626000165939331},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.5613999962806702},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.5188999772071838},{"id":"https://openalex.org/C32220436","wikidata":"https://www.wikidata.org/wiki/Q2072214","display_name":"Personalized medicine","level":2,"score":0.5031999945640564},{"id":"https://openalex.org/C163763905","wikidata":"https://www.wikidata.org/wiki/Q17075943","display_name":"Precision medicine","level":2,"score":0.4869999885559082},{"id":"https://openalex.org/C2778653478","wikidata":"https://www.wikidata.org/wiki/Q1054718","display_name":"Kidney disease","level":2,"score":0.43959999084472656},{"id":"https://openalex.org/C2781197716","wikidata":"https://www.wikidata.org/wiki/Q864574","display_name":"Biomarker","level":2,"score":0.4284000098705292},{"id":"https://openalex.org/C177769412","wikidata":"https://www.wikidata.org/wiki/Q278090","display_name":"Prior probability","level":3,"score":0.42309999465942383},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.4207000136375427},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.38940000534057617},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38089999556541443},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3691999912261963},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.3646000027656555},{"id":"https://openalex.org/C527412718","wikidata":"https://www.wikidata.org/wiki/Q855395","display_name":"Interpretation (philosophy)","level":2,"score":0.3612000048160553},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.314300000667572},{"id":"https://openalex.org/C197934379","wikidata":"https://www.wikidata.org/wiki/Q2047938","display_name":"Adverse effect","level":2,"score":0.31130000948905945},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.310699999332428},{"id":"https://openalex.org/C2779974597","wikidata":"https://www.wikidata.org/wiki/Q28448986","display_name":"Clinical Practice","level":2,"score":0.30059999227523804},{"id":"https://openalex.org/C535046627","wikidata":"https://www.wikidata.org/wiki/Q30612","display_name":"Clinical trial","level":2,"score":0.29490000009536743},{"id":"https://openalex.org/C103402496","wikidata":"https://www.wikidata.org/wiki/Q1106171","display_name":"Prediction interval","level":2,"score":0.2883000075817108},{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.2815999984741211},{"id":"https://openalex.org/C116567970","wikidata":"https://www.wikidata.org/wiki/Q864217","display_name":"Biobank","level":2,"score":0.28060001134872437},{"id":"https://openalex.org/C107908354","wikidata":"https://www.wikidata.org/wiki/Q4781456","display_name":"Applicability domain","level":3,"score":0.26899999380111694},{"id":"https://openalex.org/C177713679","wikidata":"https://www.wikidata.org/wiki/Q679690","display_name":"Intensive care medicine","level":1,"score":0.2660999894142151},{"id":"https://openalex.org/C534262118","wikidata":"https://www.wikidata.org/wiki/Q177719","display_name":"Medical diagnosis","level":2,"score":0.2615000009536743},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.2612999975681305}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:pubmedcentral.nih.gov:13229086","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13229086/","pdf_url":null,"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,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},{"id":"doi:10.48550/arxiv.2605.18701","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2605.18701","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":null,"issn":null,"is_oa":true,"is_in_doaj":false,"is_core":false,"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":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:13229086","is_oa":true,"landing_page_url":"https://pmc.ncbi.nlm.nih.gov/articles/PMC13229086/","pdf_url":null,"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,"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":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"ArXiv","raw_type":"Text"},"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Blood-based":[0],"biomarkers":[1],"underpin":[2],"clinical":[3,128],"diagnosis":[4],"and":[5,66,69,98,149,168,179,201],"management,":[6],"yet":[7],"their":[8],"interpretation":[9,25,50],"relies":[10],"largely":[11],"on":[12,144],"fixed":[13],"population":[14],"reference":[15,140],"intervals":[16,112,141,156],"that":[17,103,138,181],"ignore":[18],"stable,":[19],"intra-patient":[20],"variability.":[21],"As":[22],"such,":[23],"population-based":[24],"can":[26,70],"mask":[27],"meaningful":[28],"deviation":[29],"from":[30,87],"an":[31,202],"individual's":[32],"baseline,":[33],"risking":[34],"delayed":[35],"disease":[36],"detection.":[37],"To":[38,192],"remedy":[39],"this,":[40],"there":[41],"have":[42],"been":[43],"increasing":[44],"efforts":[45],"to":[46,60,101,117,185],"personalize":[47],"blood":[48],"biomarker":[49],"using":[51],"individual":[52,183],"testing":[53],"histories.":[54],"However,":[55],"these":[56],"methods":[57],"may":[58],"overfit":[59],"sparse":[61],"data,":[62],"inflating":[63],"false-positive":[64],"rates":[65],"unnecessary":[67],"follow-up,":[68],"also":[71],"unwittingly":[72],"include":[73],"unrecognized":[74],"or":[75],"subclinical":[76],"disease.":[77,170],"Here,":[78],"we":[79,195],"leverage":[80],"nearly":[81],"2":[82],"billion":[83],"longitudinal":[84],"laboratory":[85,105,177,209],"measurements":[86,120],"over":[88],"1.6":[89],"million":[90],"individuals":[91],"across":[92],"North":[93],"America,":[94],"the":[95,198],"Middle":[96],"East,":[97],"East":[99],"Asia,":[100],"show":[102],"while":[104],"values":[106],"are":[107],"highly":[108],"individual,":[109],"purely":[110],"personalized":[111],"routinely":[113],"overfit,":[114],"classifying":[115],"up":[116],"68%":[118],"of":[119],"as":[121],"abnormal,":[122],"without":[123],"corresponding":[124],"associations":[125],"with":[126],"adverse":[127],"outcomes.":[129],"We":[130],"then":[131],"introduce":[132],"NORMA,":[133],"a":[134,146],"conditional":[135],"transformer-based":[136],"framework":[137],"generates":[139],"by":[142],"conditioning":[143],"both":[145],"patient's":[147],"history":[148],"population-level":[150,186],"data":[151],"about":[152],"\"normal\"":[153],"variation.":[154],"NORMA-derived":[155],"achieve":[157],"higher":[158],"precision":[159],"for":[160,206],"predicting":[161],"outcomes,":[162],"including":[163],"mortality,":[164],"acute":[165],"kidney":[166],"injury,":[167],"chronic":[169],"These":[171],"findings":[172],"caution":[173],"against":[174],"over-personalization":[175],"in":[176],"medicine":[178],"demonstrate":[180],"anchoring":[182],"trajectories":[184],"priors":[187],"outperforms":[188],"either":[189],"approach":[190],"alone.":[191],"promote":[193],"transparency,":[194],"publicly":[196],"release":[197],"model,":[199],"code,":[200],"interactive":[203],"user":[204],"interface":[205],"accessible,":[207],"individualized":[208],"interpretation.":[210]},"counts_by_year":[],"updated_date":"2026-07-01T06:00:48.157686","created_date":"2026-05-20T00:00:00"}
