{"id":"https://openalex.org/W2774827735","doi":"https://doi.org/10.1109/bibm.2017.8217696","title":"Modeling heart procedures from EHRs: An application of exponential families","display_name":"Modeling heart procedures from EHRs: An application of exponential families","publication_year":2017,"publication_date":"2017-11-01","ids":{"openalex":"https://openalex.org/W2774827735","doi":"https://doi.org/10.1109/bibm.2017.8217696","mag":"2774827735"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2017.8217696","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217696","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","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/A5072696348","display_name":"Shuo Yang","orcid":"https://orcid.org/0000-0001-6406-081X"},"institutions":[{"id":"https://openalex.org/I4210119109","display_name":"Indiana University Bloomington","ror":"https://ror.org/02k40bc56","country_code":"US","type":"education","lineage":["https://openalex.org/I4210119109","https://openalex.org/I592451"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shuo Yang","raw_affiliation_strings":["School of Informatics, Computing and Engineering Indiana University, Bloomington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"School of Informatics, Computing and Engineering Indiana University, Bloomington, USA","institution_ids":["https://openalex.org/I4210119109"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5066413089","display_name":"Fabian Hadiji","orcid":"https://orcid.org/0000-0003-4564-9842"},"institutions":[{"id":"https://openalex.org/I200332995","display_name":"TU Dortmund University","ror":"https://ror.org/01k97gp34","country_code":"DE","type":"education","lineage":["https://openalex.org/I200332995"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Fabian Hadiji","raw_affiliation_strings":["TU Dortmund University, Dortmund, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Dortmund University, Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037636074","display_name":"Kristian Kersting","orcid":"https://orcid.org/0000-0002-2873-9152"},"institutions":[{"id":"https://openalex.org/I31512782","display_name":"Technische Universit\u00e4t Darmstadt","ror":"https://ror.org/05n911h24","country_code":"DE","type":"education","lineage":["https://openalex.org/I31512782"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Kristian Kersting","raw_affiliation_strings":["TU Darmstadt University, Darmstadt, Germany"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"TU Darmstadt University, Darmstadt, Germany","institution_ids":["https://openalex.org/I31512782"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053301712","display_name":"Shaun J. Grannis","orcid":"https://orcid.org/0000-0002-8093-6639"},"institutions":[{"id":"https://openalex.org/I1324682000","display_name":"Regenstrief Institute","ror":"https://ror.org/05f2ywb48","country_code":"US","type":"nonprofit","lineage":["https://openalex.org/I1324682000"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Shaun Grannis","raw_affiliation_strings":["Regenstrief Institute, Bloomington, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Regenstrief Institute, Bloomington, USA","institution_ids":["https://openalex.org/I1324682000"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5064323671","display_name":"Sriraam Natarajan","orcid":"https://orcid.org/0000-0001-9889-6260"},"institutions":[{"id":"https://openalex.org/I162577319","display_name":"The University of Texas at Dallas","ror":"https://ror.org/049emcs32","country_code":"US","type":"education","lineage":["https://openalex.org/I162577319"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sriraam Natarajan","raw_affiliation_strings":["The University of Texas at Dallas, Dallas, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Texas at Dallas, Dallas, USA","institution_ids":["https://openalex.org/I162577319"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":5,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.17707618,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"29","issue":null,"first_page":"491","last_page":"497"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T10136","display_name":"Statistical Methods and Inference","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2613","display_name":"Statistics and Probability"},"field":{"id":"https://openalex.org/fields/26","display_name":"Mathematics"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T11901","display_name":"Bayesian Methods and Mixture Models","score":0.9948999881744385,"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":0.9873999953269958,"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/exponential-family","display_name":"Exponential family","score":0.8767926692962646},{"id":"https://openalex.org/keywords/multinomial-distribution","display_name":"Multinomial distribution","score":0.7299156785011292},{"id":"https://openalex.org/keywords/categorical-variable","display_name":"Categorical variable","score":0.7128980159759521},{"id":"https://openalex.org/keywords/poisson-distribution","display_name":"Poisson distribution","score":0.6436662077903748},{"id":"https://openalex.org/keywords/exponential-distribution","display_name":"Exponential distribution","score":0.480271577835083},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.4560825824737549},{"id":"https://openalex.org/keywords/exponential-function","display_name":"Exponential function","score":0.42972731590270996},{"id":"https://openalex.org/keywords/count-data","display_name":"Count data","score":0.41901424527168274},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.3699837327003479},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.3544154167175293}],"concepts":[{"id":"https://openalex.org/C55974624","wikidata":"https://www.wikidata.org/wiki/Q1188504","display_name":"Exponential family","level":2,"score":0.8767926692962646},{"id":"https://openalex.org/C192065140","wikidata":"https://www.wikidata.org/wiki/Q1147928","display_name":"Multinomial distribution","level":2,"score":0.7299156785011292},{"id":"https://openalex.org/C5274069","wikidata":"https://www.wikidata.org/wiki/Q2285707","display_name":"Categorical variable","level":2,"score":0.7128980159759521},{"id":"https://openalex.org/C100906024","wikidata":"https://www.wikidata.org/wiki/Q205692","display_name":"Poisson distribution","level":2,"score":0.6436662077903748},{"id":"https://openalex.org/C55350006","wikidata":"https://www.wikidata.org/wiki/Q237193","display_name":"Exponential distribution","level":2,"score":0.480271577835083},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.4560825824737549},{"id":"https://openalex.org/C151376022","wikidata":"https://www.wikidata.org/wiki/Q168698","display_name":"Exponential function","level":2,"score":0.42972731590270996},{"id":"https://openalex.org/C33643355","wikidata":"https://www.wikidata.org/wiki/Q5176731","display_name":"Count data","level":3,"score":0.41901424527168274},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.3699837327003479},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.3544154167175293},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2017.8217696","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2017.8217696","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2017 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":20,"referenced_works":["https://openalex.org/W1197916086","https://openalex.org/W1511986666","https://openalex.org/W1678356000","https://openalex.org/W1980345828","https://openalex.org/W1985249003","https://openalex.org/W2010974761","https://openalex.org/W2036872051","https://openalex.org/W2043116610","https://openalex.org/W2071484198","https://openalex.org/W2100398996","https://openalex.org/W2119755537","https://openalex.org/W2120340025","https://openalex.org/W2133990480","https://openalex.org/W2154290889","https://openalex.org/W2155988679","https://openalex.org/W2466251702","https://openalex.org/W2511489337","https://openalex.org/W6675079100","https://openalex.org/W6678397451","https://openalex.org/W6682342736"],"related_works":["https://openalex.org/W2986159101","https://openalex.org/W2887997499","https://openalex.org/W3034218051","https://openalex.org/W2400574502","https://openalex.org/W4385873216","https://openalex.org/W1994299254","https://openalex.org/W4388295412","https://openalex.org/W4399572996","https://openalex.org/W2157751387","https://openalex.org/W2427622374"],"abstract_inverted_index":{"In":[0],"order":[1],"to":[2,16,96],"facilitate":[3],"better":[4],"estimations":[5],"on":[6],"coronary":[7,23],"artery":[8,24],"disease":[9],"conditions":[10],"of":[11,20,66,104],"a":[12],"patient,":[13],"we":[14,69],"aim":[15],"predict":[17],"the":[18,31,55,64,71,102],"number":[19,103],"Angioplasty":[21],"(a":[22],"procedure)":[25],"by":[26],"taking":[27],"into":[28],"account":[29],"all":[30],"information":[32],"from":[33],"his/her":[34],"Electronic":[35],"Health":[36],"Record":[37],"(EHR)":[38],"data.":[39,87],"For":[40],"this":[41,105],"purpose,":[42],"two":[43,78],"exponential":[44,67],"family":[45],"members\u2014multinomial":[46],"distribution":[47,50],"and":[48,60,80],"Poisson":[49,93],"models\u2014are":[51],"considered,":[52],"which":[53],"treat":[54],"target":[56],"variable":[57],"as":[58],"categorical-valued":[59],"count-valued":[61],"respectively.":[62],"From":[63],"perspective":[65],"family,":[68],"derive":[70],"functional":[72],"gradient":[73],"boosting":[74],"approach":[75],"for":[76,100],"these":[77],"distributions":[79],"analyze":[81],"their":[82],"assumptions":[83],"with":[84],"real":[85],"EHR":[86],"Our":[88],"empirical":[89],"results":[90],"show":[91],"that":[92],"models":[94],"appear":[95],"be":[97],"more":[98],"faithful":[99],"modeling":[101],"procedure.":[106]},"counts_by_year":[],"updated_date":"2026-06-11T09:08:48.828518","created_date":"2025-10-10T00:00:00"}
