{"id":"https://openalex.org/W2904910852","doi":"https://doi.org/10.1109/ismict.2018.8573698","title":"Deep Holistic Representation Learning from EHR","display_name":"Deep Holistic Representation Learning from EHR","publication_year":2018,"publication_date":"2018-03-01","ids":{"openalex":"https://openalex.org/W2904910852","doi":"https://doi.org/10.1109/ismict.2018.8573698","mag":"2904910852"},"language":"en","primary_location":{"id":"doi:10.1109/ismict.2018.8573698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ismict.2018.8573698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 12th International Symposium on Medical Information and Communication Technology (ISMICT)","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/A5051809190","display_name":"Edmond Zhang","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Edmond Zhang","raw_affiliation_strings":["Orion Health, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Orion Health, Auckland, New Zealand","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5069113222","display_name":"R. R. Rejimol Robinson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Reece Robinson","raw_affiliation_strings":["Orion Health, Auckland, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Orion Health, Auckland, New Zealand","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5087785022","display_name":"Bernhard Pfahringer","orcid":"https://orcid.org/0000-0002-3732-5787"},"institutions":[{"id":"https://openalex.org/I52179390","display_name":"University of Waikato","ror":"https://ror.org/013fsnh78","country_code":"NZ","type":"education","lineage":["https://openalex.org/I52179390"]}],"countries":["NZ"],"is_corresponding":false,"raw_author_name":"Bernhard Pfahringer","raw_affiliation_strings":["The University of Waikato, Hamilton, New Zealand"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"The University of Waikato, Hamilton, New Zealand","institution_ids":["https://openalex.org/I52179390"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":1,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":3,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":"2","issue":null,"first_page":"1","last_page":"6"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9998000264167786,"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":0.9998000264167786,"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/T10028","display_name":"Topic Modeling","score":0.9878000020980835,"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/T12647","display_name":"Traditional Chinese Medicine Studies","score":0.9652000069618225,"subfield":{"id":"https://openalex.org/subfields/2707","display_name":"Complementary and alternative 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/computer-science","display_name":"Computer science","score":0.6779558062553406},{"id":"https://openalex.org/keywords/representation","display_name":"Representation (politics)","score":0.503710925579071},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.4482184648513794},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.42169368267059326},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.38844379782676697},{"id":"https://openalex.org/keywords/knowledge-management","display_name":"Knowledge management","score":0.3427674472332001}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6779558062553406},{"id":"https://openalex.org/C2776359362","wikidata":"https://www.wikidata.org/wiki/Q2145286","display_name":"Representation (politics)","level":3,"score":0.503710925579071},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4482184648513794},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.42169368267059326},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.38844379782676697},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3427674472332001},{"id":"https://openalex.org/C17744445","wikidata":"https://www.wikidata.org/wiki/Q36442","display_name":"Political science","level":0,"score":0.0},{"id":"https://openalex.org/C94625758","wikidata":"https://www.wikidata.org/wiki/Q7163","display_name":"Politics","level":2,"score":0.0},{"id":"https://openalex.org/C199539241","wikidata":"https://www.wikidata.org/wiki/Q7748","display_name":"Law","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ismict.2018.8573698","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ismict.2018.8573698","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 12th International Symposium on Medical Information and Communication Technology (ISMICT)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/4","score":0.4099999964237213,"display_name":"Quality Education"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":28,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W2027106132","https://openalex.org/W2250539671","https://openalex.org/W2255847468","https://openalex.org/W2279842060","https://openalex.org/W2284851926","https://openalex.org/W2293197871","https://openalex.org/W2396881363","https://openalex.org/W2398489001","https://openalex.org/W2404901863","https://openalex.org/W2469314752","https://openalex.org/W2474925868","https://openalex.org/W2481271618","https://openalex.org/W2488984245","https://openalex.org/W2511950764","https://openalex.org/W2606022689","https://openalex.org/W2607113351","https://openalex.org/W2732226154","https://openalex.org/W2746939225","https://openalex.org/W2791043838","https://openalex.org/W2963078493","https://openalex.org/W2985962305","https://openalex.org/W3101973032","https://openalex.org/W6685812147","https://openalex.org/W6691697006","https://openalex.org/W6712207982","https://openalex.org/W6742619940","https://openalex.org/W6748797612"],"related_works":["https://openalex.org/W2731899572","https://openalex.org/W3215138031","https://openalex.org/W3009238340","https://openalex.org/W4321369474","https://openalex.org/W4360585206","https://openalex.org/W4285208911","https://openalex.org/W3082895349","https://openalex.org/W4213079790","https://openalex.org/W2248239756","https://openalex.org/W4323565446"],"abstract_inverted_index":{"In":[0],"recent":[1],"years":[2],"there":[3],"has":[4,37],"been":[5,111],"a":[6,89,160,168],"surge":[7],"of":[8,98,116,126,141,152,159],"interest":[9],"in":[10,55],"applying":[11],"deep":[12,58,79],"neural":[13,106,128,143],"networks":[14,129],"to":[15,64,92,101,120,166],"electronic":[16],"health":[17,65],"records":[18],"(EHRs)":[19],"for":[20,76,113],"predictive":[21],"clinical":[22],"tasks.":[23],"EHR":[24],"data":[25,34,100],"cannot":[26],"be":[27],"mined":[28],"like":[29],"traditional":[30],"image":[31],"or":[32],"text":[33],"because":[35],"it":[36],"unique":[38],"characteristics":[39,115],"including":[40],"temporality,":[41],"irregularity,":[42],"heterogeneity":[43],"(both":[44],"structured":[45],"and":[46,48],"unstructured)":[47],"incompleteness.":[49],"We":[50,132],"begin":[51],"by":[52],"identifying":[53],"weaknesses":[54],"the":[56,114,124,139,150],"way":[57],"learning":[59,95],"is":[60,86,130,145,164],"currently":[61],"being":[62],"applied":[63],"data.":[66],"Then,":[67,123],"leveraging":[68],"these":[69,127],"insights,":[70],"we":[71],"propose":[72],"an":[73],"end-to-end":[74],"strategy":[75,85],"extracting":[77,153],"complimentary":[78],"feature":[80],"representations":[81],"from":[82,156],"EHRs.":[83],"This":[84,147],"based":[87],"on":[88],"\u201cbringing":[90],"model":[91],"data\u201d":[93],"machine":[94],"approach":[96],"instead":[97],"\u201ctransforming":[99],"model\u201d.":[102],"It":[103],"uses":[104],"multiple":[105],"networks,":[107],"that":[108,134],"have":[109],"each":[110,142],"optimised":[112],"their":[117],"input":[118],"data,":[119],"extract":[121],"features.":[122],"output":[125,140],"combined.":[131],"show":[133],"prediction":[135],"accuracy":[136],"improves":[137],"as":[138],"network":[144],"contributed.":[146],"work":[148],"demonstrates":[149],"value":[151],"relevant":[154],"insights":[155],"different":[157],"aspects":[158],"patients":[161],"record,":[162],"which":[163],"analogous":[165],"how":[167],"clinician":[169],"makes":[170],"decisions.":[171]},"counts_by_year":[{"year":2023,"cited_by_count":2},{"year":2020,"cited_by_count":1}],"updated_date":"2026-07-14T23:27:15.235271","created_date":"2025-10-10T00:00:00"}
