{"id":"https://openalex.org/W2165692234","doi":"https://doi.org/10.1109/cicare.2013.6583061","title":"A comparison of artificial neural network, latent class analysis and logistic regression for determining which patients benefit from a cognitive behavioural approach to treatment for non-specific low back pain","display_name":"A comparison of artificial neural network, latent class analysis and logistic regression for determining which patients benefit from a cognitive behavioural approach to treatment for non-specific low back pain","publication_year":2013,"publication_date":"2013-04-01","ids":{"openalex":"https://openalex.org/W2165692234","doi":"https://doi.org/10.1109/cicare.2013.6583061","mag":"2165692234"},"language":"en","primary_location":{"id":"doi:10.1109/cicare.2013.6583061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicare.2013.6583061","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","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/A5064707493","display_name":"Martine J. Barons","orcid":"https://orcid.org/0000-0003-1483-2943"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Martine J. Barons","raw_affiliation_strings":["University of Warwick, Coventry, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5050428053","display_name":"Nick Parsons","orcid":"https://orcid.org/0000-0001-9975-888X"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Nick Parsons","raw_affiliation_strings":["University of Warwick, Coventry, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5010336667","display_name":"Frances Griffiths","orcid":"https://orcid.org/0000-0002-4173-1438"},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Frances Griffiths","raw_affiliation_strings":["University of Warwick, Coventry, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5012634537","display_name":"Margaret Thorogood","orcid":null},"institutions":[{"id":"https://openalex.org/I39555362","display_name":"University of Warwick","ror":"https://ror.org/01a77tt86","country_code":"GB","type":"education","lineage":["https://openalex.org/I39555362"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Margaret Thorogood","raw_affiliation_strings":["University of Warwick, Coventry, UK"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University of Warwick, Coventry, UK","institution_ids":["https://openalex.org/I39555362"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I39555362"],"apc_list":null,"apc_paid":null,"fwci":3.9834,"has_fulltext":false,"cited_by_count":6,"citation_normalized_percentile":{"value":0.92638889,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":89,"max":96},"biblio":{"volume":"84","issue":null,"first_page":"7","last_page":"12"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10084","display_name":"Musculoskeletal pain and rehabilitation","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},"topics":[{"id":"https://openalex.org/T10084","display_name":"Musculoskeletal pain and rehabilitation","score":0.9952999949455261,"subfield":{"id":"https://openalex.org/subfields/2736","display_name":"Pharmacology"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}},{"id":"https://openalex.org/T13309","display_name":"Reliability and Agreement in Measurement","score":0.9871000051498413,"subfield":{"id":"https://openalex.org/subfields/1804","display_name":"Statistics, Probability and Uncertainty"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/logistic-regression","display_name":"Logistic regression","score":0.7598156929016113},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6737939715385437},{"id":"https://openalex.org/keywords/latent-class-model","display_name":"Latent class model","score":0.5978491306304932},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5452483296394348},{"id":"https://openalex.org/keywords/cognition","display_name":"Cognition","score":0.45921584963798523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.42623811960220337},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.394706666469574},{"id":"https://openalex.org/keywords/statistics","display_name":"Statistics","score":0.38059571385383606},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.32199984788894653},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.18250957131385803},{"id":"https://openalex.org/keywords/psychiatry","display_name":"Psychiatry","score":0.09266474843025208}],"concepts":[{"id":"https://openalex.org/C151956035","wikidata":"https://www.wikidata.org/wiki/Q1132755","display_name":"Logistic regression","level":2,"score":0.7598156929016113},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6737939715385437},{"id":"https://openalex.org/C70727504","wikidata":"https://www.wikidata.org/wiki/Q1806878","display_name":"Latent class model","level":2,"score":0.5978491306304932},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5452483296394348},{"id":"https://openalex.org/C169900460","wikidata":"https://www.wikidata.org/wiki/Q2200417","display_name":"Cognition","level":2,"score":0.45921584963798523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42623811960220337},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.394706666469574},{"id":"https://openalex.org/C105795698","wikidata":"https://www.wikidata.org/wiki/Q12483","display_name":"Statistics","level":1,"score":0.38059571385383606},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.32199984788894653},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.18250957131385803},{"id":"https://openalex.org/C118552586","wikidata":"https://www.wikidata.org/wiki/Q7867","display_name":"Psychiatry","level":1,"score":0.09266474843025208}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/cicare.2013.6583061","is_oa":false,"landing_page_url":"https://doi.org/10.1109/cicare.2013.6583061","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2013 IEEE Symposium on Computational Intelligence in Healthcare and e-health (CICARE)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.6800000071525574,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":22,"referenced_works":["https://openalex.org/W1497939657","https://openalex.org/W1513618424","https://openalex.org/W1966329044","https://openalex.org/W1985615467","https://openalex.org/W1987138256","https://openalex.org/W1998884854","https://openalex.org/W2008519433","https://openalex.org/W2025357764","https://openalex.org/W2061468750","https://openalex.org/W2070411279","https://openalex.org/W2090330784","https://openalex.org/W2094920861","https://openalex.org/W2100014097","https://openalex.org/W2114010383","https://openalex.org/W2114898872","https://openalex.org/W2118516436","https://openalex.org/W2135026920","https://openalex.org/W2171218330","https://openalex.org/W2554987453","https://openalex.org/W2889999572","https://openalex.org/W4399640401","https://openalex.org/W6629748880"],"related_works":["https://openalex.org/W2495310989","https://openalex.org/W2129863591","https://openalex.org/W3109783536","https://openalex.org/W1596769710","https://openalex.org/W4206474224","https://openalex.org/W3102547229","https://openalex.org/W2923497059","https://openalex.org/W4293659749","https://openalex.org/W2294143590","https://openalex.org/W2978363435"],"abstract_inverted_index":{"It":[0],"can":[1,195],"be":[2,210,239],"difficult":[3],"to":[4,23,38,47,62,73,209,253,258],"select":[5],"the":[6,25,48,109,118,123,128,136,149,160,165,171,176,187,192,201,222,227,240,254],"right":[7],"treatment":[8,49,259],"for":[9,13,40,152,197,247,250],"low":[10,51,68],"back":[11,52,69,90,262],"pain":[12,53,70,91],"a":[14,42,65,74,78],"given":[15],"individual.":[16],"The":[17,82,155],"objective":[18],"of":[19,27,50,67,77,92,120,191,230,243,260],"this":[20,121,153],"study":[21],"was":[22,59,127,148],"compare":[24],"use":[26],"artificial":[28,137,156,172,223],"neural":[29,138,157,173,224],"networks":[30],"with":[31,140],"latent":[32,177],"class":[33,178],"analysis":[34],"and":[35,98,114,144,159,175,234,237],"logistic":[36,162,179],"regression":[37,163,180],"identify":[39],"whom":[41],"new,":[43],"cognitive":[44,79,255],"behavioural":[45,80,256],"approach":[46,257],"is":[54],"indicated":[55],"or":[56],"contra-indicated.":[57],"Data":[58],"made":[60],"available":[61],"us":[63],"from":[64,101],"cohort":[66],"patients":[71,252],"recruited":[72,100],"clinical":[75],"trial":[76],"approach.":[81],"701":[83],"participants":[84],"had":[85,164,181],"at":[86,93],"least":[87,94],"moderately":[88],"troublesome":[89],"6":[95],"weeks'":[96],"duration":[97],"were":[99],"56":[102],"general":[103],"practices":[104],"in":[105,108,212],"7":[106],"regions":[107],"UK":[110],"between":[111,200],"April":[112,115],"2005":[113],"2007.":[116],"For":[117],"purposes":[119],"study,":[122],"main":[124],"outcome":[125],"measure":[126],"Roland":[129],"Morris":[130],"Disability":[131],"Questionnaire.":[132],"We":[133,185,218],"found":[134],"that":[135,204,221],"network":[139,151,158,174,225],"one":[141],"hidden":[142],"node":[143],"weight":[145],"decay":[146],"0.1":[147],"optimal":[150],"application.":[154],"ordinary":[161],"lowest":[166],"overall":[167,231],"error":[168,232],"rate,":[169],"but":[170],"superior":[182,188],"log":[183,189,235],"score.":[184],"concluded":[186],"score":[190],"techniques":[193],"which":[194],"allow":[196],"nonlinear":[198],"relationships":[199],"variables":[202],"suggests":[203],"these":[205,244],"are":[206],"more":[207],"likely":[208],"useful":[211],"decision":[213,248],"support":[214,249],"than":[215],"linear":[216],"models.":[217],"have":[219],"shown":[220],"provides":[226],"best":[228,241],"combination":[229],"rate":[233],"score,":[236],"would":[238],"candidate":[242],"three":[245],"models":[246],"allocating":[251],"lower":[261],"pain.":[263]},"counts_by_year":[{"year":2022,"cited_by_count":1},{"year":2021,"cited_by_count":1},{"year":2020,"cited_by_count":1},{"year":2018,"cited_by_count":2},{"year":2014,"cited_by_count":1}],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
