{"id":"https://openalex.org/W2911956894","doi":"https://doi.org/10.1109/bibm.2018.8621252","title":"Detecting Serendipitous Drug Usage in Social Media with Deep Neural Network Models","display_name":"Detecting Serendipitous Drug Usage in Social Media with Deep Neural Network Models","publication_year":2018,"publication_date":"2018-12-01","ids":{"openalex":"https://openalex.org/W2911956894","doi":"https://doi.org/10.1109/bibm.2018.8621252","mag":"2911956894"},"language":"en","primary_location":{"id":"doi:10.1109/bibm.2018.8621252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2018.8621252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 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/A5068551338","display_name":"Boshu Ru","orcid":"https://orcid.org/0000-0001-9620-1306"},"institutions":[{"id":"https://openalex.org/I102149020","display_name":"University of North Carolina at Charlotte","ror":"https://ror.org/04dawnj30","country_code":"US","type":"education","lineage":["https://openalex.org/I102149020"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Boshu Ru","raw_affiliation_strings":["Department of Software and Information Systems, University of North Caolina at Charlotte, Charlotte, NC, USA"],"affiliations":[{"raw_affiliation_string":"Department of Software and Information Systems, University of North Caolina at Charlotte, Charlotte, NC, USA","institution_ids":["https://openalex.org/I102149020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5103234379","display_name":"Dingcheng Li","orcid":"https://orcid.org/0000-0002-1630-4338"},"institutions":[{"id":"https://openalex.org/I4210108985","display_name":"Bellevue Hospital Center","ror":"https://ror.org/01ky34z31","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1283621791","https://openalex.org/I4210086933","https://openalex.org/I4210108985"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Dingcheng Li","raw_affiliation_strings":["Baidu USA, Inc, Big Data Lab, Bellevue, WA, USA"],"affiliations":[{"raw_affiliation_string":"Baidu USA, Inc, Big Data Lab, Bellevue, WA, USA","institution_ids":["https://openalex.org/I4210108985"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5066989029","display_name":"Lixia Yao","orcid":"https://orcid.org/0000-0002-5187-6120"},"institutions":[{"id":"https://openalex.org/I4210146710","display_name":"Mayo Clinic in Florida","ror":"https://ror.org/03zzw1w08","country_code":"US","type":"healthcare","lineage":["https://openalex.org/I1330342723","https://openalex.org/I4210146710"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Lixia Yao","raw_affiliation_strings":["Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA"],"affiliations":[{"raw_affiliation_string":"Department of Health Sciences Research, Mayo Clinic, Rochester, MN, USA","institution_ids":["https://openalex.org/I4210146710"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5068551338"],"corresponding_institution_ids":["https://openalex.org/I102149020"],"apc_list":null,"apc_paid":null,"fwci":0.3258,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":{"value":0.69498527,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":"462","issue":null,"first_page":"1083","last_page":"1090"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10028","display_name":"Topic Modeling","score":0.9846000075340271,"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/T10028","display_name":"Topic Modeling","score":0.9846000075340271,"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":0.9811999797821045,"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"}},{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.955299973487854,"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/computer-science","display_name":"Computer science","score":0.7623143196105957},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.7113474607467651},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.647248387336731},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.6363133788108826},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.598336935043335},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5803190469741821},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.5606985688209534},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.538112461566925},{"id":"https://openalex.org/keywords/social-media","display_name":"Social media","score":0.5001230239868164},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.48386847972869873},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.45548194646835327},{"id":"https://openalex.org/keywords/world-wide-web","display_name":"World Wide Web","score":0.1552245020866394},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.11529257893562317}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7623143196105957},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7113474607467651},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.647248387336731},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.6363133788108826},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.598336935043335},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5803190469741821},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.5606985688209534},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.538112461566925},{"id":"https://openalex.org/C518677369","wikidata":"https://www.wikidata.org/wiki/Q202833","display_name":"Social media","level":2,"score":0.5001230239868164},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.48386847972869873},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.45548194646835327},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.1552245020866394},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.11529257893562317},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bibm.2018.8621252","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bibm.2018.8621252","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5,"display_name":"Good health and well-being","id":"https://metadata.un.org/sdg/3"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":58,"referenced_works":["https://openalex.org/W168564468","https://openalex.org/W179875071","https://openalex.org/W950853366","https://openalex.org/W1522301498","https://openalex.org/W1533861849","https://openalex.org/W1665214252","https://openalex.org/W1677182931","https://openalex.org/W1832693441","https://openalex.org/W1914219707","https://openalex.org/W1934253021","https://openalex.org/W1982945330","https://openalex.org/W2014583042","https://openalex.org/W2062941476","https://openalex.org/W2064675550","https://openalex.org/W2069976248","https://openalex.org/W2099668895","https://openalex.org/W2105050951","https://openalex.org/W2106411961","https://openalex.org/W2112946624","https://openalex.org/W2113072832","https://openalex.org/W2113765418","https://openalex.org/W2118978333","https://openalex.org/W2122402213","https://openalex.org/W2123442489","https://openalex.org/W2132083787","https://openalex.org/W2138512826","https://openalex.org/W2155157373","https://openalex.org/W2163757302","https://openalex.org/W2171469118","https://openalex.org/W2193669765","https://openalex.org/W2250539671","https://openalex.org/W2250879510","https://openalex.org/W2265846598","https://openalex.org/W2269931776","https://openalex.org/W2276005619","https://openalex.org/W2284289336","https://openalex.org/W2470673105","https://openalex.org/W2529655138","https://openalex.org/W2593565339","https://openalex.org/W2622385665","https://openalex.org/W2767106145","https://openalex.org/W2773850259","https://openalex.org/W2886875078","https://openalex.org/W2919706860","https://openalex.org/W2964121744","https://openalex.org/W4294170691","https://openalex.org/W6606906144","https://openalex.org/W6631190155","https://openalex.org/W6631943919","https://openalex.org/W6637242042","https://openalex.org/W6640015359","https://openalex.org/W6676179485","https://openalex.org/W6682691769","https://openalex.org/W6693505360","https://openalex.org/W6695662000","https://openalex.org/W6738966219","https://openalex.org/W6745643699","https://openalex.org/W6759807639"],"related_works":["https://openalex.org/W2946409105","https://openalex.org/W4226493464","https://openalex.org/W4312417841","https://openalex.org/W3152932816","https://openalex.org/W2985392712","https://openalex.org/W3133861977","https://openalex.org/W4388996947","https://openalex.org/W3133567596","https://openalex.org/W2798009317","https://openalex.org/W3203949288"],"abstract_inverted_index":{"Serendipitous":[0],"drug":[1,16,27,49,82,181,230],"usage":[2,50,83],"refers":[3],"to":[4,33,190,207],"unexpected":[5],"relief":[6],"of":[7,26,80,112,176,212,220,228,269],"comorbid":[8],"diseases":[9],"or":[10,20],"symptoms":[11],"when":[12],"patients":[13],"take":[14],"a":[15,88,99,169],"for":[17,38,68],"another":[18],"common":[19],"known":[21],"indication.":[22],"In":[23,74,217],"the":[24,109,115,120,209,218],"history":[25],"discovery,":[28],"serendipity":[29],"has":[30,44],"contributed":[31],"significantly":[32],"new":[34],"and":[35,70,92,130,137,155,161,196,225,245,272],"successful":[36],"indications":[37],"many":[39],"drugs.":[40],"Our":[41],"previous":[42],"research":[43],"identified":[45,60],"patient":[46,110],"reported":[47],"serendipitous":[48,81,180,229],"in":[51,61,84,108,259],"social":[52,62,85],"media.":[53],"If":[54],"such":[55],"information":[56,143,150,205],"could":[57,65,253],"be":[58,66],"computationally":[59],"media,":[63],"it":[64],"helpful":[67],"generating":[69],"validating":[71],"drug-repositioning":[72],"hypotheses.":[73],"this":[75],"study,":[76],"we":[77,145,184],"framed":[78],"detection":[79],"media":[86],"as":[87,98],"binary":[89],"classification":[90],"problem":[91],"investigated":[93],"deep":[94,164,187,213,232,249,265],"neural":[95,122,165,188,214,233,250,266],"network":[96,123,128,135,166,215,234,251],"models":[97,167,235,242,252],"solution.":[100],"We":[101,118,158],"constructed":[102],"word-embedding":[103],"features":[104],"from":[105,147],"drug-review":[106,148],"posts":[107],"forum":[111],"WebMD,":[113],"using":[114],"word2vec":[116],"algorithm.":[117],"adopted":[119],"convolutional":[121,131],"(CNN),":[124],"long":[125,132],"short-term":[126,133],"memory":[127,134],"(LSTM),":[129],"(CLSTM)":[136],"redesigned":[138],"them":[139],"by":[140],"adding":[141,203],"contextual":[142],"that":[144,202],"extracted":[146],"posts,":[149],"filtering":[151],"tools,":[152],"medical":[153,156],"ontology,":[154],"knowledge.":[157],"trained,":[159],"tuned,":[160],"evaluated":[162],"our":[163,186],"on":[168],"gold":[170],"standard":[171],"dataset":[172,224],"containing":[173],"15,714":[174],"sentences,":[175],"which":[177],"447":[178],"contained":[179],"usages.":[182],"Additionally,":[183],"compared":[185],"networks":[189,267],"support":[191],"vector":[192],"machine,":[193],"random":[194],"forest,":[195],"AdaBoost.M1":[197],"algorithms.":[198],"The":[199],"results":[200],"showed":[201],"context":[204,246],"helped":[206],"reduce":[208],"false-positive":[210],"rate":[211],"models.":[216],"presence":[219],"an":[221],"extremely":[222],"imbalanced":[223],"limited":[226],"instances":[227],"usage,":[231],"did":[236],"not":[237],"outperform":[238],"other":[239],"machine":[240],"learning":[241],"with":[243],"n-gram":[244],"features.":[247],"However,":[248],"more":[254],"effectively":[255],"utilize":[256],"word":[257],"embedding":[258],"feature":[260],"construction.":[261],"This":[262],"advantage":[263],"made":[264],"worthy":[268],"further":[270],"investigation":[271],"improvement.":[273]},"counts_by_year":[{"year":2024,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}