{"id":"https://openalex.org/W3157774777","doi":"https://doi.org/10.1145/3412841.3442106","title":"Classification of drug prescribing information using long short-term memory networks","display_name":"Classification of drug prescribing information using long short-term memory networks","publication_year":2021,"publication_date":"2021-03-22","ids":{"openalex":"https://openalex.org/W3157774777","doi":"https://doi.org/10.1145/3412841.3442106","mag":"3157774777"},"language":"en","primary_location":{"id":"doi:10.1145/3412841.3442106","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3442106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","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/A5063281095","display_name":"Tianen Liu","orcid":"https://orcid.org/0000-0003-3817-2839"},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Tianen Liu","raw_affiliation_strings":["Wake Forest University"],"affiliations":[{"raw_affiliation_string":"Wake Forest University","institution_ids":["https://openalex.org/I47251452"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5019570321","display_name":"Natalia Khuri","orcid":"https://orcid.org/0000-0001-9031-8124"},"institutions":[{"id":"https://openalex.org/I47251452","display_name":"Wake Forest University","ror":"https://ror.org/0207ad724","country_code":"US","type":"education","lineage":["https://openalex.org/I47251452"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Natalia Khuri","raw_affiliation_strings":["Wake Forest University"],"affiliations":[{"raw_affiliation_string":"Wake Forest University","institution_ids":["https://openalex.org/I47251452"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":2,"corresponding_author_ids":["https://openalex.org/A5063281095"],"corresponding_institution_ids":["https://openalex.org/I47251452"],"apc_list":null,"apc_paid":null,"fwci":0.2854,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.63835421,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"1086","last_page":"1089"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10737","display_name":"Health Literacy and Information Accessibility","score":0.9508000016212463,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10737","display_name":"Health Literacy and Information Accessibility","score":0.9508000016212463,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T12151","display_name":"Interpreting and Communication in Healthcare","score":0.9318000078201294,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9232000112533569,"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/classifier","display_name":"Classifier (UML)","score":0.7316699028015137},{"id":"https://openalex.org/keywords/naive-bayes-classifier","display_name":"Naive Bayes classifier","score":0.6670059561729431},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6263277530670166},{"id":"https://openalex.org/keywords/long-short-term-memory","display_name":"Long short term memory","score":0.5438539981842041},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5427865982055664},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48494842648506165},{"id":"https://openalex.org/keywords/bayes-theorem","display_name":"Bayes' theorem","score":0.48360127210617065},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.16701912879943848},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.11846822500228882},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.11597153544425964}],"concepts":[{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.7316699028015137},{"id":"https://openalex.org/C52001869","wikidata":"https://www.wikidata.org/wiki/Q812530","display_name":"Naive Bayes classifier","level":3,"score":0.6670059561729431},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6263277530670166},{"id":"https://openalex.org/C133488467","wikidata":"https://www.wikidata.org/wiki/Q6673524","display_name":"Long short term memory","level":4,"score":0.5438539981842041},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5427865982055664},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48494842648506165},{"id":"https://openalex.org/C207201462","wikidata":"https://www.wikidata.org/wiki/Q182505","display_name":"Bayes' theorem","level":3,"score":0.48360127210617065},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.16701912879943848},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.11846822500228882},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.11597153544425964},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/3412841.3442106","is_oa":false,"landing_page_url":"https://doi.org/10.1145/3412841.3442106","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 36th Annual ACM Symposium on Applied Computing","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3","score":0.8100000023841858}],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":1,"referenced_works":["https://openalex.org/W3131420649"],"related_works":["https://openalex.org/W1493451373","https://openalex.org/W2780177025","https://openalex.org/W4251019512","https://openalex.org/W2948288905","https://openalex.org/W4288333917","https://openalex.org/W2997277234","https://openalex.org/W2296661347","https://openalex.org/W2095916325","https://openalex.org/W4224221743","https://openalex.org/W1506481126"],"abstract_inverted_index":{"Information":[0],"about":[1],"drug's":[2],"safety":[3],"and":[4,12,39,50],"efficacy":[5],"is":[6],"publicly":[7],"available":[8],"to":[9,22,86,114],"healthcare":[10],"providers":[11],"consumers":[13],"in":[14,106],"the":[15,44,48,90,97,107],"United":[16],"States.":[17],"Yet,":[18],"it":[19],"remains":[20],"challenging":[21],"find":[23],"this":[24],"information":[25,112],"for":[26,47,89],"special":[27],"populations":[28,32],"of":[29,53,80,109],"patients.":[30,41],"These":[31],"include":[33],"pregnant,":[34],"lactating,":[35],"nursing":[36,115],"women,":[37],"elderly,":[38],"pediatric":[40],"Motivated":[42],"by":[43],"unmet":[45],"need":[46],"accurate":[49],"efficient":[51],"extraction":[52],"information,":[54],"we":[55],"trained":[56,100],"a":[57],"multi-class":[58],"Long":[59],"Short-Term":[60],"Memory":[61],"classifier":[62,70,94],"with":[63],"over":[64],"90,000":[65],"semi-structured":[66],"labeled":[67],"texts.":[68],"The":[69,93],"achieved":[71],"excellent":[72],"performance":[73],"when":[74],"tested":[75],"on":[76],"an":[77],"unseen":[78],"dataset":[79],"20,000":[81],"texts,":[82],"reaching":[83],"between":[84],"95%":[85],"99%":[87],"accuracy":[88],"five":[91],"classes.":[92],"significantly":[95],"outperformed":[96],"baseline":[98],"model":[99],"using":[101],"Na\u00efve":[102],"Bayes":[103],"algorithm,":[104],"especially":[105],"classification":[108],"texts":[110],"containing":[111],"relevant":[113],"mothers.":[116]},"counts_by_year":[{"year":2023,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}