{"id":"https://openalex.org/W28029454","doi":null,"title":"Causal discovery from medical textual data.","display_name":"Causal discovery from medical textual data.","publication_year":2000,"publication_date":"2000-01-01","ids":{"openalex":"https://openalex.org/W28029454","mag":"28029454","pmid":"https://pubmed.ncbi.nlm.nih.gov/11079942","pmcid":"https://www.ncbi.nlm.nih.gov/pmc/articles/2243738"},"language":"en","primary_location":{"id":"pmh:oai:pubmedcentral.nih.gov:2243738","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2243738","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,"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":"Proc AMIA Symp","raw_type":"Text"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["pubmed"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2243738","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5035264371","display_name":"Subramani Mani","orcid":null,"observed_orcids":[]},"institutions":[{"id":"https://openalex.org/I170201317","display_name":"University of Pittsburgh","ror":"https://ror.org/01an3r305","country_code":"US","type":"education","lineage":["https://openalex.org/I170201317"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"S Mani","raw_affiliation_strings":["Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh, USA. mani@cbmi.upmc.edu"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Center for Biomedical Informatics, Intelligent Systems Program, University of Pittsburgh, USA. mani@cbmi.upmc.edu","institution_ids":["https://openalex.org/I170201317"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5039985406","display_name":"Gregory F. Cooper","orcid":"https://orcid.org/0000-0002-9276-773X","observed_orcids":["https://orcid.org/0000-0002-9276-773X"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"G F Cooper","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.3997,"has_fulltext":false,"cited_by_count":28,"citation_normalized_percentile":{"value":0.66012815,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":98},"biblio":{"volume":null,"issue":null,"first_page":"542","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9696999788284302,"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/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9696999788284302,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.01720000058412552,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.00570000009611249,"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/causal-discovery","display_name":"causal discovery","score":0.9990000128746033},{"id":"https://openalex.org/keywords/clinical-text-mining","display_name":"clinical text mining","score":0.5529999732971191},{"id":"https://openalex.org/keywords/discharge-summaries","display_name":"discharge summaries","score":0.8450000286102295},{"id":"https://openalex.org/keywords/intensive-care-unit","display_name":"intensive care unit","score":0.8799999952316284},{"id":"https://openalex.org/keywords/causal-bayesian-networks","display_name":"causal Bayesian networks","score":0.9950000047683716},{"id":"https://openalex.org/keywords/causal-relationship","display_name":"causal relationship","score":0.5450000166893005},{"id":"https://openalex.org/keywords/clinical-outcomes","display_name":"clinical outcomes","score":0.7649999856948853}],"concepts":[{"id":"https://openalex.org/C11671645","wikidata":"https://www.wikidata.org/wiki/Q5054567","display_name":"Causal model","level":2,"score":0.6680750250816345},{"id":"https://openalex.org/C116834253","wikidata":"https://www.wikidata.org/wiki/Q2039217","display_name":"Identification (biology)","level":2,"score":0.6307559609413147},{"id":"https://openalex.org/C33724603","wikidata":"https://www.wikidata.org/wiki/Q812540","display_name":"Bayesian network","level":2,"score":0.5705143213272095},{"id":"https://openalex.org/C182365436","wikidata":"https://www.wikidata.org/wiki/Q50701","display_name":"Variable (mathematics)","level":2,"score":0.5541710257530212},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.5383340716362},{"id":"https://openalex.org/C158600405","wikidata":"https://www.wikidata.org/wiki/Q5054566","display_name":"Causal inference","level":2,"score":0.524231493473053},{"id":"https://openalex.org/C162144332","wikidata":"https://www.wikidata.org/wiki/Q1665305","display_name":"Instrumental variable","level":2,"score":0.452445924282074},{"id":"https://openalex.org/C163504300","wikidata":"https://www.wikidata.org/wiki/Q2364925","display_name":"Causal structure","level":2,"score":0.4479495882987976},{"id":"https://openalex.org/C64357122","wikidata":"https://www.wikidata.org/wiki/Q1149766","display_name":"Causality (physics)","level":2,"score":0.44719788432121277},{"id":"https://openalex.org/C2987525970","wikidata":"https://www.wikidata.org/wiki/Q96374569","display_name":"Causal analysis","level":2,"score":0.42195838689804077},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.4180499315261841},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.41738998889923096},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.38124319911003113},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.3774660527706146},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35975658893585205},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3369184136390686},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.2529858946800232},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.1546642780303955},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.13157513737678528},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C59822182","wikidata":"https://www.wikidata.org/wiki/Q441","display_name":"Botany","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"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/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[{"descriptor_ui":"D000465","descriptor_name":"Algorithms","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D001499","descriptor_name":"Bayes Theorem","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D006801","descriptor_name":"Humans","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D007362","descriptor_name":"Intensive Care Units","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008390","descriptor_name":"Markov Chains","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false},{"descriptor_ui":"D008499","descriptor_name":"Medical Records","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":true},{"descriptor_ui":"D010351","descriptor_name":"Patient Discharge","qualifier_ui":"Q000706","qualifier_name":"statistics & numerical data","is_major_topic":true},{"descriptor_ui":"D015984","descriptor_name":"Causality","qualifier_ui":null,"qualifier_name":null,"is_major_topic":true},{"descriptor_ui":"D016247","descriptor_name":"Information Storage and Retrieval","qualifier_ui":null,"qualifier_name":null,"is_major_topic":false}],"locations_count":4,"locations":[{"id":"pmh:oai:pubmedcentral.nih.gov:2243738","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2243738","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,"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":"Proc AMIA Symp","raw_type":"Text"},{"id":"pmid:11079942","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/11079942","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":"Proceedings. AMIA Symposium","raw_type":"Journal Article"},{"id":"pmh:oai:CiteSeerX.psu:10.1.1.24.3655","doi":null,"is_oa":false,"landing_page_url":"http://citeseerx.ist.psu.edu/viewdoc/summary?doi=10.1.1.24.3655","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"http://www.cbmi.upmc.edu/~mani/./pub/amia_fs2000.pdf","raw_type":"text"},{"id":"mag:28029454","doi":null,"is_oa":false,"landing_page_url":"https://pubmed.ncbi.nlm.nih.gov/11079942","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":null,"is_accepted":false,"is_published":false,"raw_source_name":"PubMed","raw_type":null}],"best_oa_location":{"id":"pmh:oai:pubmedcentral.nih.gov:2243738","doi":null,"is_oa":true,"landing_page_url":"https://www.ncbi.nlm.nih.gov/pmc/articles/2243738","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,"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":"Proc AMIA Symp","raw_type":"Text"},"sustainable_development_goals":[{"score":0.9537000060081482,"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being"}],"study_designs":[],"awards":[{"id":"https://openalex.org/G6567035386","display_name":"METHODS TO MODEL CAUSE AND EFFECT FROM CLINICAL DATA","funder_award_id":"R01LM006696","funder_id":"https://openalex.org/F4320337372","funder_display_name":"U.S. National Library of Medicine"},{"id":"https://openalex.org/G9000891428","display_name":"Pittsburgh Biomedical Informatics Training Program","funder_award_id":"T15LM007059","funder_id":"https://openalex.org/F4320337372","funder_display_name":"U.S. National Library of Medicine"}],"funders":[{"id":"https://openalex.org/F4320337372","display_name":"U.S. National Library of Medicine","ror":"https://ror.org/0060t0j89"}],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":71,"referenced_works":["https://openalex.org/W28199031","https://openalex.org/W177760475","https://openalex.org/W1050094696","https://openalex.org/W1482377996","https://openalex.org/W1507419621","https://openalex.org/W1543558171","https://openalex.org/W1549529615","https://openalex.org/W1587405077","https://openalex.org/W1636667730","https://openalex.org/W1731673821","https://openalex.org/W1790346767","https://openalex.org/W1963477449","https://openalex.org/W1966584749","https://openalex.org/W1969484820","https://openalex.org/W1978027515","https://openalex.org/W1981646498","https://openalex.org/W1984358839","https://openalex.org/W1986152287","https://openalex.org/W1993862514","https://openalex.org/W1994264254","https://openalex.org/W1994522590","https://openalex.org/W1995419026","https://openalex.org/W1996537470","https://openalex.org/W2002337671","https://openalex.org/W2007052731","https://openalex.org/W2008934892","https://openalex.org/W2012395649","https://openalex.org/W2013437052","https://openalex.org/W2015496181","https://openalex.org/W2016961151","https://openalex.org/W2018141721","https://openalex.org/W2020820364","https://openalex.org/W2028366452","https://openalex.org/W2030169115","https://openalex.org/W2035087085","https://openalex.org/W2038132790","https://openalex.org/W2044403342","https://openalex.org/W2044755432","https://openalex.org/W2047516436","https://openalex.org/W2051172440","https://openalex.org/W2054732079","https://openalex.org/W2055208823","https://openalex.org/W2059698287","https://openalex.org/W2073659298","https://openalex.org/W2075435443","https://openalex.org/W2080008464","https://openalex.org/W2084129189","https://openalex.org/W2085116009","https://openalex.org/W2085766808","https://openalex.org/W2090008708","https://openalex.org/W2098542740","https://openalex.org/W2106119979","https://openalex.org/W2109833582","https://openalex.org/W2112968897","https://openalex.org/W2127441956","https://openalex.org/W2128078219","https://openalex.org/W2140774372","https://openalex.org/W2164993104","https://openalex.org/W2165284805","https://openalex.org/W2168652002","https://openalex.org/W2170979934","https://openalex.org/W2171961624","https://openalex.org/W2172040168","https://openalex.org/W2184540011","https://openalex.org/W2241488854","https://openalex.org/W2276208800","https://openalex.org/W2279775030","https://openalex.org/W2295664972","https://openalex.org/W2316988591","https://openalex.org/W2593855156","https://openalex.org/W6630265425"],"related_works":["https://openalex.org/W2898061518","https://openalex.org/W7094097059","https://openalex.org/W6903917443","https://openalex.org/W4393363250","https://openalex.org/W8643228","https://openalex.org/W7096044617","https://openalex.org/W323956050","https://openalex.org/W2087422107","https://openalex.org/W2799843582","https://openalex.org/W198951864"],"abstract_inverted_index":{"Medical":[0],"records":[1],"usually":[2],"incorporate":[3],"investigative":[4],"reports,":[5],"historical":[6],"notes,":[7],"patient":[8],"encounters":[9],"or":[10],"discharge":[11,28,110],"summaries":[12,29,111],"as":[13,87,112,125],"textual":[14],"data.":[15],"This":[16],"study":[17],"focused":[18],"on":[19],"learning":[20],"causal":[21,36,60,75,80,120],"relationships":[22,81,123],"from":[23],"intensive":[24],"care":[25],"unit":[26],"(ICU)":[27],"of":[30,34,38,56,74,94],"1611":[31],"patients.":[32],"Identification":[33],"the":[35,54,64,72,95,104,109],"factors":[37],"clinical":[39],"conditions":[40],"and":[41,50,91],"outcomes":[42],"can":[43],"help":[44],"us":[45],"formulate":[46],"better":[47],"management,":[48],"prevention":[49],"control":[51],"strategies":[52],"for":[53,114],"improvement":[55],"health":[57],"care.":[58],"For":[59],"discovery":[61],"we":[62],"applied":[63],"Local":[65],"Causal":[66],"Discovery":[67],"(LCD)":[68],"algorithm,":[69],"which":[70],"uses":[71],"framework":[73],"Bayesian":[76],"Networks":[77],"to":[78,130],"represent":[79],"among":[82],"model":[83],"variables.":[84],"LCD":[85,116],"takes":[86],"input":[88],"a":[89],"dataset":[90],"outputs":[92],"causes":[93],"form":[96],"variable":[97,101],"Y":[98],"causally":[99,133],"influences":[100],"Z.":[102],"Using":[103],"words":[105],"that":[106],"occur":[107],"in":[108],"attributes":[113],"input,":[115],"output":[117],"8":[118],"purported":[119],"relationships.":[121],"The":[122],"ranked":[124],"most":[126,132],"probable":[127],"subjectively":[128],"appear":[129],"be":[131],"plausible.":[134]},"counts_by_year":[{"year":2025,"cited_by_count":3},{"year":2024,"cited_by_count":2},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":5},{"year":2020,"cited_by_count":3},{"year":2016,"cited_by_count":1},{"year":2015,"cited_by_count":2},{"year":2013,"cited_by_count":1},{"year":2012,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2016-06-24T00:00:00"}