{"id":"https://openalex.org/W2151107649","doi":"https://doi.org/10.1109/cbms.2013.6627826","title":"Using probabilistic logic and the principle of maximum entropy for the analysis of clinical brain tumor data","display_name":"Using probabilistic logic and the principle of maximum entropy for the analysis of clinical brain tumor data","publication_year":2013,"publication_date":"2013-06-01","ids":{"openalex":"https://openalex.org/W2151107649","doi":"https://doi.org/10.1109/cbms.2013.6627826","mag":"2151107649"},"language":"en","primary_location":{"id":"doi:10.1109/cbms.2013.6627826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2013.6627826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","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/A5033176798","display_name":"Julian Varghese","orcid":"https://orcid.org/0000-0002-7206-3719"},"institutions":[{"id":"https://openalex.org/I120691247","display_name":"University of Hagen","ror":"https://ror.org/04tkkr536","country_code":"DE","type":"education","lineage":["https://openalex.org/I120691247"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Julian Varghese","raw_affiliation_strings":["Fernuniversit\u00e4t in Hagen, Hagen, Germany"],"affiliations":[{"raw_affiliation_string":"Fernuniversit\u00e4t in Hagen, Hagen, Germany","institution_ids":["https://openalex.org/I120691247"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5002320735","display_name":"Christoph Beierle","orcid":"https://orcid.org/0000-0002-0736-8516"},"institutions":[{"id":"https://openalex.org/I120691247","display_name":"University of Hagen","ror":"https://ror.org/04tkkr536","country_code":"DE","type":"education","lineage":["https://openalex.org/I120691247"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Christoph Beierle","raw_affiliation_strings":["Fernuniversit\u00e4t in Hagen, Hagen, Germany"],"affiliations":[{"raw_affiliation_string":"Fernuniversit\u00e4t in Hagen, Hagen, Germany","institution_ids":["https://openalex.org/I120691247"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050709868","display_name":"Nico Potyka","orcid":"https://orcid.org/0000-0003-1749-5233"},"institutions":[{"id":"https://openalex.org/I120691247","display_name":"University of Hagen","ror":"https://ror.org/04tkkr536","country_code":"DE","type":"education","lineage":["https://openalex.org/I120691247"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nico Potyka","raw_affiliation_strings":["Fernuniversit\u00e4t in Hagen, Hagen, Germany"],"affiliations":[{"raw_affiliation_string":"Fernuniversit\u00e4t in Hagen, Hagen, Germany","institution_ids":["https://openalex.org/I120691247"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5024120048","display_name":"Gabriele Kern-Isberner","orcid":"https://orcid.org/0000-0001-8689-5391"},"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":"Gabriele Kern-Isberner","raw_affiliation_strings":["Technische Universita\u00a8t Dortmund, Germany"],"affiliations":[{"raw_affiliation_string":"Technische Universita\u00a8t Dortmund, Germany","institution_ids":["https://openalex.org/I200332995"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5033176798"],"corresponding_institution_ids":["https://openalex.org/I120691247"],"apc_list":null,"apc_paid":null,"fwci":0.9618,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.83050448,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":95},"biblio":{"volume":null,"issue":null,"first_page":"401","last_page":"404"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11303","display_name":"Bayesian Modeling and Causal Inference","score":0.9990000128746033,"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":0.9990000128746033,"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":0.9894999861717224,"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/T10215","display_name":"Semantic Web and Ontologies","score":0.9783999919891357,"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/probabilistic-logic","display_name":"Probabilistic logic","score":0.7983831763267517},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.623482346534729},{"id":"https://openalex.org/keywords/principle-of-maximum-entropy","display_name":"Principle of maximum entropy","score":0.5648013949394226},{"id":"https://openalex.org/keywords/knowledge-base","display_name":"Knowledge base","score":0.5438868999481201},{"id":"https://openalex.org/keywords/entropy","display_name":"Entropy (arrow of time)","score":0.5038763880729675},{"id":"https://openalex.org/keywords/medical-knowledge","display_name":"Medical knowledge","score":0.4909346401691437},{"id":"https://openalex.org/keywords/domain-knowledge","display_name":"Domain knowledge","score":0.44970154762268066},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.44186705350875854},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3980519771575928},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.3514866828918457},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.14061617851257324}],"concepts":[{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.7983831763267517},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.623482346534729},{"id":"https://openalex.org/C9679016","wikidata":"https://www.wikidata.org/wiki/Q1417473","display_name":"Principle of maximum entropy","level":2,"score":0.5648013949394226},{"id":"https://openalex.org/C4554734","wikidata":"https://www.wikidata.org/wiki/Q593744","display_name":"Knowledge base","level":2,"score":0.5438868999481201},{"id":"https://openalex.org/C106301342","wikidata":"https://www.wikidata.org/wiki/Q4117933","display_name":"Entropy (arrow of time)","level":2,"score":0.5038763880729675},{"id":"https://openalex.org/C2985722590","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medical knowledge","level":2,"score":0.4909346401691437},{"id":"https://openalex.org/C207685749","wikidata":"https://www.wikidata.org/wiki/Q2088941","display_name":"Domain knowledge","level":2,"score":0.44970154762268066},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.44186705350875854},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3980519771575928},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.3514866828918457},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.14061617851257324},{"id":"https://openalex.org/C509550671","wikidata":"https://www.wikidata.org/wiki/Q126945","display_name":"Medical education","level":1,"score":0.0},{"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/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cbms.2013.6627826","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cbms.2013.6627826","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 26th IEEE International Symposium on Computer-Based Medical Systems","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/16","score":0.7900000214576721,"display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":14,"referenced_works":["https://openalex.org/W48427358","https://openalex.org/W76332699","https://openalex.org/W94633634","https://openalex.org/W635669621","https://openalex.org/W1492518391","https://openalex.org/W1511986666","https://openalex.org/W1560814177","https://openalex.org/W2003373550","https://openalex.org/W2036892044","https://openalex.org/W2068962858","https://openalex.org/W2110293626","https://openalex.org/W2160382843","https://openalex.org/W4249099522","https://openalex.org/W6603898396"],"related_works":["https://openalex.org/W2385621242","https://openalex.org/W2367629516","https://openalex.org/W2086580554","https://openalex.org/W2553860513","https://openalex.org/W2014940746","https://openalex.org/W1986001501","https://openalex.org/W2353740909","https://openalex.org/W2475408106","https://openalex.org/W2592721119","https://openalex.org/W4386298164"],"abstract_inverted_index":{"Dealing":[0],"with":[1,100],"uncertainty":[2],"that":[3,61],"is":[4,11,81,98],"inherently":[5],"present":[6],"in":[7,85],"any":[8],"medical":[9,20,34,63,74],"domain,":[10],"one":[12],"of":[13,40,94,105],"the":[14,38,92],"major":[15],"challenges":[16],"when":[17],"designing":[18],"a":[19,49,57,103,112],"decision":[21],"support":[22],"system.":[23],"We":[24,46],"demonstrate":[25],"how":[26],"probabilistic":[27,52],"logic":[28],"can":[29],"be":[30],"used":[31],"to":[32,55,102],"design":[33],"knowledge":[35,58,64,80],"bases":[36],"at":[37],"example":[39],"analysing":[41],"clinical":[42],"brain":[43],"tumor":[44],"data.":[45],"use":[47],"MECoRe,":[48],"system":[50],"implementing":[51],"conditional":[53],"logic,":[54],"create":[56],"base":[59],"BT":[60,97],"contains":[62],"originating":[65],"from":[66,73],"both":[67],"statistical":[68],"data":[69],"as":[70,72],"well":[71],"experts.":[75],"Any":[76],"incomplete":[77],"or":[78],"unspecified":[79],"completed":[82],"by":[83,90],"MECoRe":[84],"an":[86],"information-theoretically":[87],"optimal":[88],"way":[89],"employing":[91],"principle":[93],"maximum":[95],"entropy.":[96],"evaluated":[99],"respect":[101],"series":[104],"queries":[106],"regarding":[107],"diagnosis":[108],"and":[109],"prognosis,":[110],"using":[111],"real":[113],"documented":[114],"patient":[115],"case.":[116]},"counts_by_year":[{"year":2025,"cited_by_count":1},{"year":2015,"cited_by_count":1},{"year":2013,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}