{"id":"https://openalex.org/W2251905489","doi":"https://doi.org/10.18653/v1/w15-2617","title":"Adverse Drug Event classification of health records using dictionary based pre-processing and machine learning","display_name":"Adverse Drug Event classification of health records using dictionary based pre-processing and machine learning","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2251905489","doi":"https://doi.org/10.18653/v1/w15-2617","mag":"2251905489"},"language":"en","primary_location":{"id":"doi:10.18653/v1/w15-2617","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-2617","pdf_url":"https://www.aclweb.org/anthology/W15-2617.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"gold","oa_url":"https://www.aclweb.org/anthology/W15-2617.pdf","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5048624339","display_name":"Stefanie Friedrich","orcid":"https://orcid.org/0000-0002-3889-5589"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":false,"raw_author_name":"Stefanie Friedrich","raw_affiliation_strings":["Department of Computer and Systems Sciences (DSV) Stockholm University P.O. Box 7003 164 07 Kista Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Systems Sciences (DSV) Stockholm University P.O. Box 7003 164 07 Kista Sweden","institution_ids":["https://openalex.org/I161593684"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5010279607","display_name":"Hercules Dalianis","orcid":"https://orcid.org/0000-0003-0165-9926"},"institutions":[{"id":"https://openalex.org/I161593684","display_name":"Stockholm University","ror":"https://ror.org/05f0yaq80","country_code":"SE","type":"education","lineage":["https://openalex.org/I161593684"]}],"countries":["SE"],"is_corresponding":true,"raw_author_name":"Hercules Dalianis","raw_affiliation_strings":["Department of Computer and Systems Sciences (DSV) Stockholm University P.O. Box 7003 164 07 Kista Sweden"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Computer and Systems Sciences (DSV) Stockholm University P.O. Box 7003 164 07 Kista Sweden","institution_ids":["https://openalex.org/I161593684"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":["https://openalex.org/A5010279607"],"corresponding_institution_ids":["https://openalex.org/I161593684"],"apc_list":null,"apc_paid":null,"fwci":3.5307,"has_fulltext":true,"cited_by_count":6,"citation_normalized_percentile":{"value":0.91739917,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":90,"max":97},"biblio":{"volume":null,"issue":null,"first_page":"121","last_page":"130"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},"topics":[{"id":"https://openalex.org/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9930999875068665,"subfield":{"id":"https://openalex.org/subfields/1312","display_name":"Molecular Biology"},"field":{"id":"https://openalex.org/fields/13","display_name":"Biochemistry, Genetics and Molecular Biology"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}},{"id":"https://openalex.org/T11652","display_name":"Imbalanced Data Classification Techniques","score":0.9904000163078308,"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/T11943","display_name":"Pharmacovigilance and Adverse Drug Reactions","score":0.9842000007629395,"subfield":{"id":"https://openalex.org/subfields/3005","display_name":"Toxicology"},"field":{"id":"https://openalex.org/fields/30","display_name":"Pharmacology, Toxicology and Pharmaceutics"},"domain":{"id":"https://openalex.org/domains/1","display_name":"Life Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7239944934844971},{"id":"https://openalex.org/keywords/health-records","display_name":"Health records","score":0.657752513885498},{"id":"https://openalex.org/keywords/false-positive-paradox","display_name":"False positive paradox","score":0.6446629762649536},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.6299551725387573},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6099984645843506},{"id":"https://openalex.org/keywords/recall","display_name":"Recall","score":0.6061269044876099},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5261501669883728},{"id":"https://openalex.org/keywords/decision-tree","display_name":"Decision tree","score":0.5253438353538513},{"id":"https://openalex.org/keywords/class","display_name":"Class (philosophy)","score":0.5125036239624023},{"id":"https://openalex.org/keywords/tree","display_name":"Tree (set theory)","score":0.4444468021392822},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.4370183050632477},{"id":"https://openalex.org/keywords/word","display_name":"Word (group theory)","score":0.43012818694114685},{"id":"https://openalex.org/keywords/f1-score","display_name":"F1 score","score":0.417962908744812},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.12554961442947388},{"id":"https://openalex.org/keywords/health-care","display_name":"Health care","score":0.11270970106124878},{"id":"https://openalex.org/keywords/psychology","display_name":"Psychology","score":0.08312880992889404}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7239944934844971},{"id":"https://openalex.org/C3019952477","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Health records","level":3,"score":0.657752513885498},{"id":"https://openalex.org/C64869954","wikidata":"https://www.wikidata.org/wiki/Q1859747","display_name":"False positive paradox","level":2,"score":0.6446629762649536},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.6299551725387573},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6099984645843506},{"id":"https://openalex.org/C100660578","wikidata":"https://www.wikidata.org/wiki/Q18733","display_name":"Recall","level":2,"score":0.6061269044876099},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5261501669883728},{"id":"https://openalex.org/C84525736","wikidata":"https://www.wikidata.org/wiki/Q831366","display_name":"Decision tree","level":2,"score":0.5253438353538513},{"id":"https://openalex.org/C2777212361","wikidata":"https://www.wikidata.org/wiki/Q5127848","display_name":"Class (philosophy)","level":2,"score":0.5125036239624023},{"id":"https://openalex.org/C113174947","wikidata":"https://www.wikidata.org/wiki/Q2859736","display_name":"Tree (set theory)","level":2,"score":0.4444468021392822},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.4370183050632477},{"id":"https://openalex.org/C90805587","wikidata":"https://www.wikidata.org/wiki/Q10944557","display_name":"Word (group theory)","level":2,"score":0.43012818694114685},{"id":"https://openalex.org/C148524875","wikidata":"https://www.wikidata.org/wiki/Q6975395","display_name":"F1 score","level":2,"score":0.417962908744812},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.12554961442947388},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.11270970106124878},{"id":"https://openalex.org/C15744967","wikidata":"https://www.wikidata.org/wiki/Q9418","display_name":"Psychology","level":0,"score":0.08312880992889404},{"id":"https://openalex.org/C180747234","wikidata":"https://www.wikidata.org/wiki/Q23373","display_name":"Cognitive psychology","level":1,"score":0.0},{"id":"https://openalex.org/C2524010","wikidata":"https://www.wikidata.org/wiki/Q8087","display_name":"Geometry","level":1,"score":0.0},{"id":"https://openalex.org/C134306372","wikidata":"https://www.wikidata.org/wiki/Q7754","display_name":"Mathematical analysis","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.18653/v1/w15-2617","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-2617","pdf_url":"https://www.aclweb.org/anthology/W15-2617.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis","raw_type":"proceedings-article"}],"best_oa_location":{"id":"doi:10.18653/v1/w15-2617","is_oa":true,"landing_page_url":"https://doi.org/10.18653/v1/w15-2617","pdf_url":"https://www.aclweb.org/anthology/W15-2617.pdf","source":null,"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the Sixth International Workshop on Health Text Mining and Information Analysis","raw_type":"proceedings-article"},"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.8100000023841858}],"awards":[{"id":"https://openalex.org/G3903407082","display_name":"Dataanalys f\u00f6r detektion av l\u00e4kemedelseffekter","funder_award_id":"IIS11-0053","funder_id":"https://openalex.org/F4320320940","funder_display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning"}],"funders":[{"id":"https://openalex.org/F4320320940","display_name":"Stiftelsen f\u00f6r\u00a0Strategisk Forskning","ror":"https://ror.org/044wr7g58"},{"id":"https://openalex.org/F4320325669","display_name":"Stockholms Universitet","ror":"https://ror.org/05f0yaq80"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2251905489.pdf","grobid_xml":"https://content.openalex.org/works/W2251905489.grobid-xml"},"referenced_works_count":24,"referenced_works":["https://openalex.org/W97146189","https://openalex.org/W1736726159","https://openalex.org/W1835740130","https://openalex.org/W1867448996","https://openalex.org/W1978515398","https://openalex.org/W1987610593","https://openalex.org/W2080995941","https://openalex.org/W2101013878","https://openalex.org/W2125055259","https://openalex.org/W2125097277","https://openalex.org/W2129767020","https://openalex.org/W2135102212","https://openalex.org/W2149863648","https://openalex.org/W2153635508","https://openalex.org/W2154723843","https://openalex.org/W2160194473","https://openalex.org/W2166423441","https://openalex.org/W2211265567","https://openalex.org/W2251674747","https://openalex.org/W2395911948","https://openalex.org/W2412241793","https://openalex.org/W2911964244","https://openalex.org/W3120421331","https://openalex.org/W4300601563"],"related_works":["https://openalex.org/W1557094818","https://openalex.org/W3193043704","https://openalex.org/W4386259002","https://openalex.org/W1546989560","https://openalex.org/W4366990902","https://openalex.org/W4317732970","https://openalex.org/W4388550696","https://openalex.org/W4321636153","https://openalex.org/W4313289487","https://openalex.org/W4224262160"],"abstract_inverted_index":{"A":[0,16],"method":[1,87],"to":[2,50],"find":[3,51],"adverse":[4],"drug":[5],"reactions":[6],"in":[7,12,95],"electronic":[8],"health":[9,20],"records":[10,21,29],"written":[11],"Swedish":[13],"is":[14,100],"presented.":[15],"total":[17],"of":[18,74,79,84,90,103],"14,751":[19],"were":[22,48],"manually":[23,38],"classified":[24],"into":[25],"four":[26],"groups.":[27],"The":[28,61],"are":[30],"normalised":[31],"by":[32],"pre-processing":[33],"using":[34],"both":[35],"dictionaries":[36],"and":[37,59,76,81],"created":[39],"word":[40],"lists.":[41],"Three":[42],"different":[43],"supervised":[44],"machine":[45],"learning":[46],"algorithm":[47],"used":[49],"the":[52],"best":[53,62],"results;":[54],"decision":[55],"tree,":[56],"random":[57],"forest":[58],"LibSVM.":[60],"performance":[63],"on":[64],"a":[65,72,77,82,96],"test":[66],"dataset":[67,98],"was":[68],"with":[69],"LibSVM":[70],"obtaining":[71],"precision":[73],"0.69":[75],"recall":[78],"0.66,":[80],"F-score":[83],"0.67.":[85],"Our":[86],"found":[88],"865":[89],"981":[91],"true":[92],"positives":[93],"(88.2%)":[94],"3-class":[97],"which":[99],"an":[101],"improvement":[102],"49.5%":[104],"over":[105],"previous":[106],"approaches.":[107]},"counts_by_year":[{"year":2023,"cited_by_count":1},{"year":2020,"cited_by_count":2},{"year":2018,"cited_by_count":3}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
