{"id":"https://openalex.org/W1996000772","doi":"https://doi.org/10.1145/1882992.1883111","title":"Boosting-based discovery of multi-component physiological indicators","display_name":"Boosting-based discovery of multi-component physiological indicators","publication_year":2010,"publication_date":"2010-11-11","ids":{"openalex":"https://openalex.org/W1996000772","doi":"https://doi.org/10.1145/1882992.1883111","mag":"1996000772"},"language":"en","primary_location":{"id":"doi:10.1145/1882992.1883111","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1882992.1883111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM International Health Informatics Symposium","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/A5112076746","display_name":"Valeriy Gavrishchaka","orcid":null},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Valeriy V. Gavrishchaka","raw_affiliation_strings":["West Virginia University, Morgantown, WV, USA"],"affiliations":[{"raw_affiliation_string":"West Virginia University, Morgantown, WV, USA","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5035853695","display_name":"M. E. Koepke","orcid":"https://orcid.org/0000-0001-9631-356X"},"institutions":[{"id":"https://openalex.org/I12097938","display_name":"West Virginia University","ror":"https://ror.org/011vxgd24","country_code":"US","type":"education","lineage":["https://openalex.org/I12097938"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Mark E. Koepke","raw_affiliation_strings":["West Virginia University, Morgantown, WV, USA"],"affiliations":[{"raw_affiliation_string":"West Virginia University, Morgantown, WV, USA","institution_ids":["https://openalex.org/I12097938"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5029751725","display_name":"Olga N. Ulyanova","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Olga N. Ulyanova","raw_affiliation_strings":["Plekhanov Russian Academy of Economics, Moscow, Russian Fed"],"affiliations":[{"raw_affiliation_string":"Plekhanov Russian Academy of Economics, Moscow, Russian Fed","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":3,"corresponding_author_ids":["https://openalex.org/A5112076746"],"corresponding_institution_ids":["https://openalex.org/I12097938"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":2,"citation_normalized_percentile":{"value":0.07982103,"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":"790","last_page":"799"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T12205","display_name":"Time Series Analysis and Forecasting","score":0.9890999794006348,"subfield":{"id":"https://openalex.org/subfields/1711","display_name":"Signal Processing"},"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.9775999784469604,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"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/T10745","display_name":"Heart Rate Variability and Autonomic Control","score":0.9632999897003174,"subfield":{"id":"https://openalex.org/subfields/2705","display_name":"Cardiology and Cardiovascular Medicine"},"field":{"id":"https://openalex.org/fields/27","display_name":"Medicine"},"domain":{"id":"https://openalex.org/domains/4","display_name":"Health Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/interpretability","display_name":"Interpretability","score":0.8846930265426636},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.762964129447937},{"id":"https://openalex.org/keywords/boosting","display_name":"Boosting (machine learning)","score":0.7437247633934021},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5446455478668213},{"id":"https://openalex.org/keywords/component","display_name":"Component (thermodynamics)","score":0.5393486022949219},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.5173898935317993},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4714661240577698},{"id":"https://openalex.org/keywords/data-modeling","display_name":"Data modeling","score":0.43836769461631775},{"id":"https://openalex.org/keywords/stability","display_name":"Stability (learning theory)","score":0.4231567978858948},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.41899242997169495}],"concepts":[{"id":"https://openalex.org/C2781067378","wikidata":"https://www.wikidata.org/wiki/Q17027399","display_name":"Interpretability","level":2,"score":0.8846930265426636},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.762964129447937},{"id":"https://openalex.org/C46686674","wikidata":"https://www.wikidata.org/wiki/Q466303","display_name":"Boosting (machine learning)","level":2,"score":0.7437247633934021},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5446455478668213},{"id":"https://openalex.org/C168167062","wikidata":"https://www.wikidata.org/wiki/Q1117970","display_name":"Component (thermodynamics)","level":2,"score":0.5393486022949219},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.5173898935317993},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4714661240577698},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.43836769461631775},{"id":"https://openalex.org/C112972136","wikidata":"https://www.wikidata.org/wiki/Q7595718","display_name":"Stability (learning theory)","level":2,"score":0.4231567978858948},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.41899242997169495},{"id":"https://openalex.org/C77088390","wikidata":"https://www.wikidata.org/wiki/Q8513","display_name":"Database","level":1,"score":0.0},{"id":"https://openalex.org/C115961682","wikidata":"https://www.wikidata.org/wiki/Q860623","display_name":"Image (mathematics)","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},{"id":"https://openalex.org/C97355855","wikidata":"https://www.wikidata.org/wiki/Q11473","display_name":"Thermodynamics","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1145/1882992.1883111","is_oa":false,"landing_page_url":"https://doi.org/10.1145/1882992.1883111","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Proceedings of the 1st ACM International Health Informatics Symposium","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":38,"referenced_works":["https://openalex.org/W4952878","https://openalex.org/W198713922","https://openalex.org/W595099189","https://openalex.org/W610664722","https://openalex.org/W1489802977","https://openalex.org/W1498183065","https://openalex.org/W1502083951","https://openalex.org/W1506806321","https://openalex.org/W1517729176","https://openalex.org/W1532883599","https://openalex.org/W1594005194","https://openalex.org/W1663973292","https://openalex.org/W1821811819","https://openalex.org/W1875817757","https://openalex.org/W1965139407","https://openalex.org/W1970698820","https://openalex.org/W1994243985","https://openalex.org/W2026480276","https://openalex.org/W2035823308","https://openalex.org/W2057417611","https://openalex.org/W2059851411","https://openalex.org/W2077204677","https://openalex.org/W2077605628","https://openalex.org/W2078083914","https://openalex.org/W2092426871","https://openalex.org/W2116002001","https://openalex.org/W2125969986","https://openalex.org/W2221831393","https://openalex.org/W2489431170","https://openalex.org/W2492307518","https://openalex.org/W2496752087","https://openalex.org/W2505665596","https://openalex.org/W2606413316","https://openalex.org/W3033474747","https://openalex.org/W4240184530","https://openalex.org/W4285719527","https://openalex.org/W4402103832","https://openalex.org/W6629411509"],"related_works":["https://openalex.org/W2905433371","https://openalex.org/W2888392564","https://openalex.org/W4310278675","https://openalex.org/W4388422664","https://openalex.org/W4390569940","https://openalex.org/W4361193272","https://openalex.org/W2963326959","https://openalex.org/W4388685194","https://openalex.org/W2066625485","https://openalex.org/W2171641484"],"abstract_inverted_index":{"Increasing":[0],"availability":[1],"of":[2,19,27,42,72,79,98,111,131,139,162,174,184,196,215,234,247],"multi-scale":[3],"and":[4,32,36,40,58,74,90,100,136,159,207,224,244,275,284],"multi-channel":[5,59],"physiological":[6,61,80],"data":[7,57,81,132,175,185],"opens":[8],"new":[9,112],"horizons":[10],"for":[11,51,77,88,151,186,241],"quantitative":[12,115],"modeling":[13,116,149,201],"in":[14,64,117,199,266],"medicine.":[15],"However,":[16,114,177],"practical":[17,193,239],"limitations":[18,127],"existing":[20,221],"approaches":[21],"include":[22],"both":[23],"the":[24,28,37,43,129],"low":[25],"accuracy":[26],"simplified":[29,222],"analytical":[30],"models":[31,223],"empirical":[33,227],"expert-defined":[34],"rules":[35],"insufficient":[38],"interpretability":[39],"stability":[41],"pure":[44],"data-driven":[45],"models.":[46],"Such":[47],"challenges":[48,198],"are":[49,147,160,213],"typical":[50],"automated":[52],"diagnostics":[53,92,243],"from":[54,143,167,220,250],"high-resolution":[55],"image":[56],"temporal":[60],"information":[62],"available":[63],"modern":[65],"clinical":[66],"settings.":[67],"In":[68],"addition,":[69],"increasing":[70],"number":[71,130,173],"portable":[73],"wearable":[75],"systems":[76,154],"collection":[78],"outside":[82],"medical":[83],"facilities":[84],"provide":[85],"an":[86,232],"opportunity":[87],"express":[89,242,274],"remote":[91,276],"as":[93,95],"well":[94],"early":[96,245],"detection":[97,246],"irregular":[99],"transient":[101],"patterns":[102],"caused":[103],"by":[104,205],"developing":[105],"abnormalities":[106],"or":[107,170],"subtle":[108],"initial":[109],"effects":[110],"treatments.":[113],"such":[118],"applications":[119,268],"is":[120],"even":[121],"more":[122],"challenging":[123],"due":[124],"to":[125,237,270],"obvious":[126],"on":[128],"channels,":[133],"increased":[134],"noise":[135],"non-stationary":[137],"nature":[138],"considered":[140],"tasks.":[141],"Methods":[142],"nonlinear":[144],"dynamics":[145],"(NLD)":[146],"natural":[148],"tools":[150],"adaptive":[152],"biological":[153],"with":[155],"multiple":[156],"feedback":[157],"loops":[158],"capable":[161,214],"inferring":[163],"essential":[164],"dynamic":[165],"properties":[166],"just":[168],"one":[169],"a":[171,238,263],"small":[172],"channels.":[176],"most":[178],"NLD":[179],"indicators":[180],"require":[181],"long":[182],"periods":[183],"stable":[187],"calculation":[188],"which":[189],"significantly":[190],"limits":[191],"their":[192],"value.":[194],"Many":[195],"these":[197],"biomedical":[200],"could":[202,261],"be":[203],"overcome":[204],"boosting":[206],"similar":[208],"ensemble":[209],"learning":[210],"techniques":[211],"that":[212],"discovering":[216],"robust":[217],"multi-component":[218],"meta-models":[219],"other":[225],"incomplete":[226],"knowledge.":[228],"Here":[229],"we":[230],"describe":[231],"application":[233],"this":[235],"approach":[236],"system":[240,260],"treatment":[248,282],"responses":[249],"short":[251],"beat-to-beat":[252],"heart":[253],"rate":[254],"(RR)":[255],"time":[256],"series.":[257],"The":[258],"proposed":[259],"play":[262],"key":[264],"role":[265],"many":[267],"relevant":[269],"e-healthcare,":[271],"personalized":[272],"medicine,":[273],"web-enabled":[277],"diagnostics,":[278],"decision":[279],"support":[280],"systems,":[281],"optimization":[283],"others.":[285]},"counts_by_year":[{"year":2022,"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"}
