{"id":"https://openalex.org/W2955988535","doi":"https://doi.org/10.1109/ichi.2019.8904646","title":"Learning Deep Representations from Clinical Data for Chronic Kidney Disease","display_name":"Learning Deep Representations from Clinical Data for Chronic Kidney Disease","publication_year":2019,"publication_date":"2019-06-01","ids":{"openalex":"https://openalex.org/W2955988535","doi":"https://doi.org/10.1109/ichi.2019.8904646","mag":"2955988535"},"language":"en","primary_location":{"id":"doi:10.1109/ichi.2019.8904646","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichi.2019.8904646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Healthcare Informatics (ICHI)","raw_type":"proceedings-article"},"type":"conference-paper","indexed_in":["arxiv","crossref","datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://arxiv.org/pdf/1810.00490","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5034235300","display_name":"Duc Thanh Anh Luong","orcid":"https://orcid.org/0000-0003-4768-5089"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Duc Thanh Anh Luong","raw_affiliation_strings":["University at Buffalo,Department of Computer Science and Engineering,Buffalo,New York,14260","University at Buffalo,Department of Computer Science and Engineering,,Buffalo,,New York,14260"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo,Department of Computer Science and Engineering,Buffalo,New York,14260","institution_ids":["https://openalex.org/I63190737"]},{"raw_affiliation_string":"University at Buffalo,Department of Computer Science and Engineering,,Buffalo,,New York,14260","institution_ids":["https://openalex.org/I63190737"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5003851078","display_name":"Varun Chandola","orcid":"https://orcid.org/0000-0001-8990-1398"},"institutions":[{"id":"https://openalex.org/I63190737","display_name":"University at Buffalo, State University of New York","ror":"https://ror.org/01y64my43","country_code":"US","type":"education","lineage":["https://openalex.org/I63190737"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Varun Chandola","raw_affiliation_strings":["University at Buffalo,Department of Computer Science and Engineering,Buffalo,New York,14260","University at Buffalo,Department of Computer Science and Engineering,,Buffalo,,New York,14260"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"University at Buffalo,Department of Computer Science and Engineering,Buffalo,New York,14260","institution_ids":["https://openalex.org/I63190737"]},{"raw_affiliation_string":"University at Buffalo,Department of Computer Science and Engineering,,Buffalo,,New York,14260","institution_ids":["https://openalex.org/I63190737"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":["https://openalex.org/I63190737"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":true,"cited_by_count":0,"citation_normalized_percentile":{"value":0.06193005,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"1","last_page":"10"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998999834060669,"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":0.9998999834060669,"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/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.9923999905586243,"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"}},{"id":"https://openalex.org/T10775","display_name":"Generative Adversarial Networks and Image Synthesis","score":0.9682000279426575,"subfield":{"id":"https://openalex.org/subfields/1707","display_name":"Computer Vision and Pattern Recognition"},"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/autoencoder","display_name":"Autoencoder","score":0.821082353591919},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.6417561173439026},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.6170933246612549},{"id":"https://openalex.org/keywords/kidney-disease","display_name":"Kidney disease","score":0.6107378005981445},{"id":"https://openalex.org/keywords/context","display_name":"Context (archaeology)","score":0.5826278924942017},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5748633146286011},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.5642815828323364},{"id":"https://openalex.org/keywords/key","display_name":"Key (lock)","score":0.4875149428844452},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.48635536432266235},{"id":"https://openalex.org/keywords/population","display_name":"Population","score":0.4594593346118927},{"id":"https://openalex.org/keywords/noise","display_name":"Noise (video)","score":0.4543440043926239},{"id":"https://openalex.org/keywords/term","display_name":"Term (time)","score":0.42958688735961914},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.42720115184783936},{"id":"https://openalex.org/keywords/disease","display_name":"Disease","score":0.42199116945266724},{"id":"https://openalex.org/keywords/medicine","display_name":"Medicine","score":0.23890963196754456},{"id":"https://openalex.org/keywords/internal-medicine","display_name":"Internal medicine","score":0.11897751688957214}],"concepts":[{"id":"https://openalex.org/C101738243","wikidata":"https://www.wikidata.org/wiki/Q786435","display_name":"Autoencoder","level":3,"score":0.821082353591919},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6417561173439026},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.6170933246612549},{"id":"https://openalex.org/C2778653478","wikidata":"https://www.wikidata.org/wiki/Q1054718","display_name":"Kidney disease","level":2,"score":0.6107378005981445},{"id":"https://openalex.org/C2779343474","wikidata":"https://www.wikidata.org/wiki/Q3109175","display_name":"Context (archaeology)","level":2,"score":0.5826278924942017},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5748633146286011},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.5642815828323364},{"id":"https://openalex.org/C26517878","wikidata":"https://www.wikidata.org/wiki/Q228039","display_name":"Key (lock)","level":2,"score":0.4875149428844452},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.48635536432266235},{"id":"https://openalex.org/C2908647359","wikidata":"https://www.wikidata.org/wiki/Q2625603","display_name":"Population","level":2,"score":0.4594593346118927},{"id":"https://openalex.org/C99498987","wikidata":"https://www.wikidata.org/wiki/Q2210247","display_name":"Noise (video)","level":3,"score":0.4543440043926239},{"id":"https://openalex.org/C61797465","wikidata":"https://www.wikidata.org/wiki/Q1188986","display_name":"Term (time)","level":2,"score":0.42958688735961914},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.42720115184783936},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.42199116945266724},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.23890963196754456},{"id":"https://openalex.org/C126322002","wikidata":"https://www.wikidata.org/wiki/Q11180","display_name":"Internal medicine","level":1,"score":0.11897751688957214},{"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/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C99454951","wikidata":"https://www.wikidata.org/wiki/Q932068","display_name":"Environmental health","level":1,"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/C38652104","wikidata":"https://www.wikidata.org/wiki/Q3510521","display_name":"Computer security","level":1,"score":0.0},{"id":"https://openalex.org/C151730666","wikidata":"https://www.wikidata.org/wiki/Q7205","display_name":"Paleontology","level":1,"score":0.0},{"id":"https://openalex.org/C86803240","wikidata":"https://www.wikidata.org/wiki/Q420","display_name":"Biology","level":0,"score":0.0}],"mesh":[],"locations_count":4,"locations":[{"id":"doi:10.1109/ichi.2019.8904646","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ichi.2019.8904646","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2019 IEEE International Conference on Healthcare Informatics (ICHI)","raw_type":"proceedings-article"},{"id":"pmh:oai:arXiv.org:1810.00490","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.00490","pdf_url":"https://arxiv.org/pdf/1810.00490","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},{"id":"mag:2955988535","is_oa":true,"landing_page_url":"https://arxiv.org/pdf/1810.00490","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"arXiv (Cornell University)","raw_type":null},{"id":"doi:10.48550/arxiv.1810.00490","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.1810.00490","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"pmh:oai:arXiv.org:1810.00490","is_oa":true,"landing_page_url":"http://arxiv.org/abs/1810.00490","pdf_url":"https://arxiv.org/pdf/1810.00490","source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"text"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320306076","display_name":"National Science Foundation","ror":"https://ror.org/021nxhr62"}],"has_content":{"grobid_xml":false,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2955988535.pdf"},"referenced_works_count":17,"referenced_works":["https://openalex.org/W1987971958","https://openalex.org/W2064675550","https://openalex.org/W2118982023","https://openalex.org/W2153579005","https://openalex.org/W2168630917","https://openalex.org/W2284851926","https://openalex.org/W2396881363","https://openalex.org/W2731341027","https://openalex.org/W2742491462","https://openalex.org/W2755758115","https://openalex.org/W2806153517","https://openalex.org/W2962732961","https://openalex.org/W2964010366","https://openalex.org/W6682691769","https://openalex.org/W6720664952","https://openalex.org/W6740040768","https://openalex.org/W6752237317"],"related_works":["https://openalex.org/W2990853488","https://openalex.org/W2893377089","https://openalex.org/W2995015263","https://openalex.org/W2141173017","https://openalex.org/W2742491462","https://openalex.org/W2566895423","https://openalex.org/W2910647020","https://openalex.org/W3101755570","https://openalex.org/W2740445537","https://openalex.org/W3131669966","https://openalex.org/W2726375170","https://openalex.org/W2980111163","https://openalex.org/W3046849288","https://openalex.org/W2527718449","https://openalex.org/W2973047679","https://openalex.org/W2766922817","https://openalex.org/W3000356826","https://openalex.org/W2756270408","https://openalex.org/W2911565143","https://openalex.org/W2894178883"],"abstract_inverted_index":{"We":[0,26],"study":[1],"the":[2,15,32,46,49,56,69,90,98,102,107,110,118,122,127,137],"behavior":[3],"of":[4,17,48,68,121],"a":[5,11,28],"Time-Aware":[6],"Long":[7],"Short-Term":[8],"Memory":[9],"Autoencoder,":[10],"state-of-the-art":[12],"method,":[13],"in":[14,31,55,101,109],"context":[16],"learning":[18],"latent":[19,119],"representations":[20,58,120],"from":[21,76,126,136],"irregularly":[22],"sampled":[23],"patient":[24,134],"data.":[25],"identify":[27,132],"key":[29],"issue":[30,50],"way":[33],"such":[34],"recurrent":[35],"neural":[36],"network":[37],"models":[38],"are":[39],"being":[40],"currently":[41],"used":[42],"and":[43,62],"show":[44,88,114],"that":[45,89,115],"solution":[47],"leads":[51],"to":[52,96],"significant":[53],"improvements":[54],"learnt":[57],"on":[59],"both":[60],"synthetic":[61],"real":[63],"datasets.":[64],"A":[65],"detailed":[66],"analysis":[67],"improved":[70],"methodology":[71],"for":[72],"representing":[73],"patients":[74,124],"suffering":[75],"Chronic":[77],"Kidney":[78],"Disease":[79],"(CKD)":[80],"using":[81,117],"clinical":[82],"data":[83],"is":[84,94],"provided.":[85],"Experimental":[86],"results":[87],"proposed":[91],"T-LSTM":[92,128],"model":[93],"able":[95],"capture":[97],"long-term":[99],"trends":[100],"data,":[103],"while":[104],"effectively":[105],"handling":[106],"noise":[108],"signal.":[111],"Finally,":[112],"we":[113],"by":[116],"CKD":[123],"obtained":[125],"autoencoder,":[129],"one":[130],"can":[131],"unusual":[133],"profiles":[135],"target":[138],"population.":[139]},"counts_by_year":[],"updated_date":"2026-07-29T14:22:42.915294","created_date":"2025-10-10T00:00:00"}
