{"id":"https://openalex.org/W3139093952","doi":"https://doi.org/10.1109/bigdata50022.2020.9377834","title":"Toward Interpretable Machine Learning for Understanding Epidemic Data","display_name":"Toward Interpretable Machine Learning for Understanding Epidemic Data","publication_year":2020,"publication_date":"2020-12-10","ids":{"openalex":"https://openalex.org/W3139093952","doi":"https://doi.org/10.1109/bigdata50022.2020.9377834","mag":"3139093952"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata50022.2020.9377834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","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/A5061058579","display_name":"Dean F. Hougen","orcid":"https://orcid.org/0000-0001-5393-1480"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Dean Frederick Hougen","raw_affiliation_strings":["School of Computer Science, University of Oklahoma, Norman, OK, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5078503673","display_name":"Jin\u2010Song Pei","orcid":"https://orcid.org/0000-0002-1042-1859"},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jin-Song Pei","raw_affiliation_strings":["School of Civil Engrg.& Environ. Science, University of Oklahoma, Norman, OK, USA"],"affiliations":[{"raw_affiliation_string":"School of Civil Engrg.& Environ. Science, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5077471957","display_name":"Sai Teja Kanneganti","orcid":null},"institutions":[{"id":"https://openalex.org/I8692664","display_name":"University of Oklahoma","ror":"https://ror.org/02aqsxs83","country_code":"US","type":"education","lineage":["https://openalex.org/I8692664"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Sai Teja Kanneganti","raw_affiliation_strings":["School of Computer Science, University of Oklahoma, Norman, OK, USA"],"affiliations":[{"raw_affiliation_string":"School of Computer Science, University of Oklahoma, Norman, OK, USA","institution_ids":["https://openalex.org/I8692664"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5061058579"],"corresponding_institution_ids":["https://openalex.org/I8692664"],"apc_list":null,"apc_paid":null,"fwci":0.1326,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.58491223,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":93},"biblio":{"volume":null,"issue":null,"first_page":"3677","last_page":"3681"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9934999942779541,"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/T12026","display_name":"Explainable Artificial Intelligence (XAI)","score":0.9934999942779541,"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/T11512","display_name":"Anomaly Detection Techniques and Applications","score":0.988099992275238,"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/T11206","display_name":"Model Reduction and Neural Networks","score":0.9860000014305115,"subfield":{"id":"https://openalex.org/subfields/3109","display_name":"Statistical and Nonlinear Physics"},"field":{"id":"https://openalex.org/fields/31","display_name":"Physics and Astronomy"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.7151957750320435},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7091284990310669},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6990644335746765},{"id":"https://openalex.org/keywords/generalization","display_name":"Generalization","score":0.5390085577964783},{"id":"https://openalex.org/keywords/domain","display_name":"Domain (mathematical analysis)","score":0.5088110566139221},{"id":"https://openalex.org/keywords/initialization","display_name":"Initialization","score":0.5060216784477234},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.4701291024684906},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.46577876806259155},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.10784858465194702}],"concepts":[{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.7151957750320435},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7091284990310669},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6990644335746765},{"id":"https://openalex.org/C177148314","wikidata":"https://www.wikidata.org/wiki/Q170084","display_name":"Generalization","level":2,"score":0.5390085577964783},{"id":"https://openalex.org/C36503486","wikidata":"https://www.wikidata.org/wiki/Q11235244","display_name":"Domain (mathematical analysis)","level":2,"score":0.5088110566139221},{"id":"https://openalex.org/C114466953","wikidata":"https://www.wikidata.org/wiki/Q6034165","display_name":"Initialization","level":2,"score":0.5060216784477234},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.4701291024684906},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.46577876806259155},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.10784858465194702},{"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/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata50022.2020.9377834","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata50022.2020.9377834","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2020 IEEE International Conference on Big Data (Big Data)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.8299999833106995,"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":30,"referenced_works":["https://openalex.org/W1501127839","https://openalex.org/W1555580699","https://openalex.org/W1965635483","https://openalex.org/W1973631422","https://openalex.org/W1985114162","https://openalex.org/W1990034511","https://openalex.org/W1991320608","https://openalex.org/W2002096058","https://openalex.org/W2031585281","https://openalex.org/W2043310303","https://openalex.org/W2049910381","https://openalex.org/W2079224763","https://openalex.org/W2103496339","https://openalex.org/W2123801958","https://openalex.org/W2128235725","https://openalex.org/W2137983211","https://openalex.org/W2166116275","https://openalex.org/W2945976633","https://openalex.org/W3016806151","https://openalex.org/W3033618701","https://openalex.org/W3035217138","https://openalex.org/W3042906734","https://openalex.org/W3070570617","https://openalex.org/W3085302951","https://openalex.org/W3130418414","https://openalex.org/W3146803896","https://openalex.org/W4233968047","https://openalex.org/W4236966694","https://openalex.org/W4247265502","https://openalex.org/W6790881081"],"related_works":["https://openalex.org/W3204184292","https://openalex.org/W3176564347","https://openalex.org/W1985458517","https://openalex.org/W2355833770","https://openalex.org/W3031039437","https://openalex.org/W183202219","https://openalex.org/W3095877357","https://openalex.org/W2072565696","https://openalex.org/W2050451745","https://openalex.org/W2378903222"],"abstract_inverted_index":{"The":[0],"COVID-19":[1],"pandemic":[2],"is":[3,103,160],"a":[4,104,108,135,189,194,200],"worldwide":[5],"crisis":[6],"with":[7],"impacts":[8,121],"that":[9,22,63,114],"are":[10,23],"both":[11],"devastating":[12],"and":[13,29,44,58,61,110,126,163,177],"inequitable":[14],"as":[15],"effects":[16],"often":[17],"fall":[18],"hardest":[19],"on":[20,92,129,192],"communities":[21],"already":[24],"suffering":[25],"from":[26,147],"economic,":[27],"social,":[28],"political":[30],"disparities.":[31],"Interpretable":[32],"machine":[33],"learning":[34],"(IML)":[35],"offers":[36],"the":[37,54,158,167],"possibility":[38],"for":[39],"detailed":[40],"understanding":[41],"of":[42,72,122,140,157,169,184],"this":[43,88],"similar":[45],"disease":[46,130],"outbreaks,":[47],"allowing":[48],"subject":[49],"matter":[50],"experts":[51],"to":[52,85,117,119,174,199],"explore":[53],"data":[55,159],"more":[56],"thoroughly":[57],"find":[59],"patterns":[60],"connections":[62],"might":[64],"otherwise":[65],"remain":[66],"hidden.":[67],"As":[68],"an":[69,97,181],"active":[70],"area":[71],"research":[73],"in":[74,143,166],"artificial":[75,99],"intelligence,":[76],"IML":[77,141],"has":[78],"great":[79],"significance":[80],"yet":[81],"numerous":[82],"technical":[83],"challenges":[84],"overcome.":[86],"In":[87],"paper,":[89],"we":[90,115,187],"focus":[91],"approximating":[93,193],"epidemic":[94,196],"curves":[95],"using":[96],"interpretable":[98,111],"neural":[100,171],"network.":[101],"This":[102],"first":[105],"step":[106],"toward":[107],"flexible":[109],"modeling":[112],"framework":[113],"plan":[116],"use":[118],"study":[120,191],"various":[123],"demographic,":[124],"socioeconomic,":[125],"other":[127],"factors":[128],"outbreaks.":[131],"We":[132],"tap":[133],"into":[134],"substantial":[136],"but":[137],"little-known":[138],"collection":[139],"studies":[142],"nonlinear":[144],"function":[145],"approximation":[146],"engineering":[148],"mechanics,":[149],"where":[150],"domain":[151],"knowledge":[152],"including":[153],"visually":[154],"observable":[155],"features":[156],"systematically":[161],"sorted":[162],"directly":[164],"utilized":[165],"initialization":[168],"sigmoidal":[170],"networks":[172],"leading":[173,198],"training":[175],"success":[176],"good":[178],"generalization.":[179],"After":[180],"introductory":[182],"review":[183],"existing":[185],"work,":[186],"present":[188],"feasibility":[190],"particular":[195],"curve":[197],"promising":[201],"result.":[202]},"counts_by_year":[{"year":2021,"cited_by_count":1}],"updated_date":"2025-11-06T03:46:38.306776","created_date":"2025-10-10T00:00:00"}
