{"id":"https://openalex.org/W2363917130","doi":"https://doi.org/10.1145/2939672.2939866","title":"Data-driven Automatic Treatment Regimen Development and Recommendation","display_name":"Data-driven Automatic Treatment Regimen Development and Recommendation","publication_year":2016,"publication_date":"2016-08-08","ids":{"openalex":"https://openalex.org/W2363917130","doi":"https://doi.org/10.1145/2939672.2939866","mag":"2363917130"},"language":"en","primary_location":{"id":"doi:10.1145/2939672.2939866","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939866","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"},"type":"conference-paper","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/A5081275566","display_name":"Leilei Sun","orcid":"https://orcid.org/0000-0002-0157-1716"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Leilei Sun","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5033864788","display_name":"Chuanren Liu","orcid":"https://orcid.org/0000-0001-9030-8495"},"institutions":[{"id":"https://openalex.org/I72816309","display_name":"Drexel University","ror":"https://ror.org/04bdffz58","country_code":"US","type":"education","lineage":["https://openalex.org/I72816309"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Chuanren Liu","raw_affiliation_strings":["Drexel University, Philadelphia, PA, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Drexel University, Philadelphia, PA, USA","institution_ids":["https://openalex.org/I72816309"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5028839347","display_name":"Chonghui Guo","orcid":"https://orcid.org/0000-0002-5155-1297"},"institutions":[{"id":"https://openalex.org/I27357992","display_name":"Dalian University of Technology","ror":"https://ror.org/023hj5876","country_code":"CN","type":"education","lineage":["https://openalex.org/I27357992"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Chonghui Guo","raw_affiliation_strings":["Dalian University of Technology, Dalian, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Dalian University of Technology, Dalian, China","institution_ids":["https://openalex.org/I27357992"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101862104","display_name":"Hui Xiong","orcid":"https://orcid.org/0000-0001-6016-6465"},"institutions":[{"id":"https://openalex.org/I102322142","display_name":"Rutgers, The State University of New Jersey","ror":"https://ror.org/05vt9qd57","country_code":"US","type":"education","lineage":["https://openalex.org/I102322142"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Hui Xiong","raw_affiliation_strings":["Rutgers University, Newark, NJ, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Rutgers University, Newark, NJ, USA","institution_ids":["https://openalex.org/I102322142"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5040056657","display_name":"Yan\u2010Ming Xie","orcid":"https://orcid.org/0000-0001-5359-4242"},"institutions":[{"id":"https://openalex.org/I200296433","display_name":"Chinese Academy of Medical Sciences & Peking Union Medical College","ror":"https://ror.org/02drdmm93","country_code":"CN","type":"education","lineage":["https://openalex.org/I200296433"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yanming Xie","raw_affiliation_strings":["China Academy of Chinese Medical Sciences, Beijing, China"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"China Academy of Chinese Medical Sciences, Beijing, China","institution_ids":["https://openalex.org/I200296433"]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":4,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":63,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1865","last_page":"1874"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9994999766349792,"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/T13702","display_name":"Machine Learning in Healthcare","score":0.9994999766349792,"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/T11710","display_name":"Biomedical Text Mining and Ontologies","score":0.9933000206947327,"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/T10350","display_name":"Electronic Health Records Systems","score":0.9715999960899353,"subfield":{"id":"https://openalex.org/subfields/3605","display_name":"Health Information Management"},"field":{"id":"https://openalex.org/fields/36","display_name":"Health Professions"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7129493951797485},{"id":"https://openalex.org/keywords/regimen","display_name":"Regimen","score":0.6904298663139343},{"id":"https://openalex.org/keywords/medical-record","display_name":"Medical record","score":0.6522552371025085},{"id":"https://openalex.org/keywords/cluster-analysis","display_name":"Cluster analysis","score":0.6395689845085144},{"id":"https://openalex.org/keywords/code","display_name":"Code (set theory)","score":0.47744137048721313},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.4288903772830963},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.4170396327972412},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.3352985680103302},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.29761803150177},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.2536659836769104},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11919271945953369}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7129493951797485},{"id":"https://openalex.org/C2781413609","wikidata":"https://www.wikidata.org/wiki/Q7308373","display_name":"Regimen","level":2,"score":0.6904298663139343},{"id":"https://openalex.org/C195910791","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Medical record","level":2,"score":0.6522552371025085},{"id":"https://openalex.org/C73555534","wikidata":"https://www.wikidata.org/wiki/Q622825","display_name":"Cluster analysis","level":2,"score":0.6395689845085144},{"id":"https://openalex.org/C2776760102","wikidata":"https://www.wikidata.org/wiki/Q5139990","display_name":"Code (set theory)","level":3,"score":0.47744137048721313},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.4288903772830963},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.4170396327972412},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.3352985680103302},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.29761803150177},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.2536659836769104},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11919271945953369},{"id":"https://openalex.org/C199360897","wikidata":"https://www.wikidata.org/wiki/Q9143","display_name":"Programming language","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/C177264268","wikidata":"https://www.wikidata.org/wiki/Q1514741","display_name":"Set (abstract data type)","level":2,"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:10.1145/2939672.2939866","is_oa":false,"landing_page_url":"https://doi.org/10.1145/2939672.2939866","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","score":0.4699999988079071,"display_name":"Good health and well-being"}],"awards":[{"id":"https://openalex.org/G1614381702","display_name":null,"funder_award_id":"71329201, 71171030,71421001","funder_id":"https://openalex.org/F4320321001","funder_display_name":"National Natural Science Foundation of China"}],"funders":[{"id":"https://openalex.org/F4320321001","display_name":"National Natural Science Foundation of China","ror":"https://ror.org/01h0zpd94"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W183688296","https://openalex.org/W204788298","https://openalex.org/W1506451709","https://openalex.org/W1799934227","https://openalex.org/W1964043357","https://openalex.org/W1998249118","https://openalex.org/W2004355295","https://openalex.org/W2004910511","https://openalex.org/W2005464046","https://openalex.org/W2011430131","https://openalex.org/W2086626648","https://openalex.org/W2089529115","https://openalex.org/W2097747115","https://openalex.org/W2110893883","https://openalex.org/W2140190241","https://openalex.org/W2158396349","https://openalex.org/W2165232124","https://openalex.org/W2165835468","https://openalex.org/W2184719144","https://openalex.org/W2217968126","https://openalex.org/W2248204980","https://openalex.org/W2400311702"],"related_works":["https://openalex.org/W2384274503","https://openalex.org/W2410163895","https://openalex.org/W2386504342","https://openalex.org/W4298130764","https://openalex.org/W2752085855","https://openalex.org/W2377136501","https://openalex.org/W2412005057","https://openalex.org/W2804364458","https://openalex.org/W2394784562","https://openalex.org/W4310603196"],"abstract_inverted_index":{"The":[0,179,196],"analysis":[1],"of":[2,25,111,137,173],"large-scale":[3,129,193],"Electrical":[4],"Medical":[5],"Records":[6],"(EMRs)":[7],"has":[8],"the":[9,59,79,104,149,155,164,215,220,233],"potential":[10],"to":[11,102,127,147,230,238],"develop":[12,143],"and":[13,31,48,85,113,132,153,202,210,232],"optimize":[14],"clinical":[15,91],"treatment":[16,19,50,107,130,139,151,158,190,194,198,207,218],"regimens.":[17],"A":[18],"regimen":[20,159,208],"usually":[21],"includes":[22],"a":[23,39,49,55,99,134,144],"series":[24],"doctor":[26,40,63,83,117],"orders":[27,64,84],"containing":[28],"rich":[29,80],"temporal":[30],"heterogeneous":[32],"information.":[33],"However,":[34],"in":[35,62,82,116,224],"many":[36],"existing":[37],"studies,":[38],"order":[41],"is":[42,52,65],"simplified":[43,53],"as":[44,54],"an":[45,122],"event":[46],"code":[47,56],"record":[51],"sequence.":[57],"Thus,":[58],"information":[60,81,115],"inherent":[61],"not":[66],"fully":[67],"used":[68],"for":[69,89,160,206],"in-depth":[70],"analysis.":[71],"In":[72,163],"this":[73,94],"paper,":[74],"we":[75,96,120,142,167],"aim":[76],"at":[77],"exploiting":[78],"developing":[86],"data-driven":[87],"approaches":[88],"improving":[90],"treatments.":[92],"To":[93],"end,":[95],"first":[97],"propose":[98,121],"novel":[100],"method":[101,185],"measure":[103],"similarities":[105],"between":[106],"records":[108],"with":[109,171],"consideration":[110],"sequential":[112],"multifaceted":[114],"orders.":[118],"Then,":[119],"efficient":[123],"density-based":[124],"clustering":[125],"algorithm":[126],"summarize":[128],"records,":[131],"extract":[133,188],"semantic":[135],"representation":[136],"each":[138],"cluster.":[140],"Finally,":[141],"unified":[145],"framework":[146],"evaluate":[148],"discovered":[150],"regimens,":[152,219],"find":[154],"most":[156,216],"effective":[157,217,234],"new":[161],"patients.":[162],"empirical":[165],"study,":[166],"validate":[168],"our":[169,225],"methods":[170],"EMRs":[172],"27,678":[174],"patients":[175],"from":[176,192,228],"14":[177],"hospitals.":[178],"results":[180],"show":[181],"that:":[182],"1)":[183],"Our":[184],"can":[186],"successfully":[187],"typical":[189],"regimens":[191,199],"records.":[195],"extracted":[197],"are":[200],"intuitive":[201],"provide":[203],"managerial":[204],"implications":[205],"design":[209],"optimization.":[211],"2)":[212],"By":[213],"recommending":[214],"total":[221],"cure":[222],"rate":[223,235],"data":[226],"improves":[227],"19.89%":[229],"21.28%,":[231],"increases":[236],"up":[237],"98.29%.":[239]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":4},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":8},{"year":2022,"cited_by_count":9},{"year":2021,"cited_by_count":4},{"year":2020,"cited_by_count":9},{"year":2019,"cited_by_count":7},{"year":2018,"cited_by_count":11},{"year":2017,"cited_by_count":3}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}