{"id":"https://openalex.org/W2803232992","doi":null,"title":"Learning Better Clinical Risk Models.","display_name":"Learning Better Clinical Risk Models.","publication_year":2015,"publication_date":"2015-01-01","ids":{"openalex":"https://openalex.org/W2803232992","mag":"2803232992"},"language":"en","primary_location":{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/113326","doi":null,"is_oa":false,"landing_page_url":"http://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"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":"Thesis"},"volume":null,"type":"dissertation","is_traditional":true,"indexed_in":[],"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/A5036384375","display_name":"Alexander Van Esbroeck","orcid":"https://orcid.org/0000-0003-4190-9985","observed_orcids":["https://orcid.org/0000-0003-4190-9985"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Alexander Van Esbroeck","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5036384375"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T13702","display_name":"Machine Learning in Healthcare","score":0.9818999767303467,"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":0.9818999767303467,"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":0.016300000250339508,"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/T11021","display_name":"ECG Monitoring and Analysis","score":0.00039999998989515007,"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/clinical-risk-model","display_name":"clinical risk model","score":0.9779999852180481},{"id":"https://openalex.org/keywords/risk-stratification","display_name":"risk stratification","score":0.6200000047683716},{"id":"https://openalex.org/keywords/cardiac-risk-prediction","display_name":"cardiac risk prediction","score":0.6980000138282776},{"id":"https://openalex.org/keywords/cardiac-death-prediction","display_name":"cardiac death prediction","score":0.8379999995231628},{"id":"https://openalex.org/keywords/wearable-physiological-monitoring","display_name":"wearable physiological monitoring","score":0.6179999709129333},{"id":"https://openalex.org/keywords/heart-rate","display_name":"heart rate","score":0.9179999828338623},{"id":"https://openalex.org/keywords/topic-modeling","display_name":"topic modeling","score":0.9179999828338623},{"id":"https://openalex.org/keywords/hidden-states","display_name":"hidden states","score":0.7429999709129333},{"id":"https://openalex.org/keywords/convolutional-neural-networks","display_name":"convolutional neural networks","score":0.9959999918937683},{"id":"https://openalex.org/keywords/sleep-recording","display_name":"sleep recording","score":0.9079999923706055},{"id":"https://openalex.org/keywords/missing-data","display_name":"missing data","score":0.9309999942779541},{"id":"https://openalex.org/keywords/trauma-patients","display_name":"trauma patients","score":0.8629999756813049},{"id":"https://openalex.org/keywords/health-care-resource-allocation","display_name":"healthcare resource allocation","score":0.6290000081062317}],"concepts":[{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.3509405851364136},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.3456839919090271}],"mesh":[],"locations_count":2,"locations":[{"id":"pmh:oai:deepblue.lib.umich.edu:2027.42/113326","doi":null,"is_oa":false,"landing_page_url":"http://deepblue.lib.umich.edu/bitstream/2027.42/113326/1/alexve_1.pdf","pdf_url":null,"source":{"id":"https://openalex.org/S4306400393","display_name":"Deep Blue (University of Michigan)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":"https://openalex.org/I27837315","host_organization_name":"University of Michigan","host_organization_lineage":["https://openalex.org/I27837315"],"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":"Thesis"},{"id":"mag:2803232992","doi":null,"is_oa":false,"landing_page_url":"https://deepblue.lib.umich.edu/handle/2027.42/113326","pdf_url":null,"source":null,"license":null,"license_id":null,"version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":null}],"best_oa_location":null,"sustainable_development_goals":[{"id":"https://metadata.un.org/sdg/3","display_name":"Good health and well-being","score":0.9537000060081482}],"study_designs":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":44,"referenced_works":["https://openalex.org/W125763627","https://openalex.org/W1503971991","https://openalex.org/W1511602449","https://openalex.org/W1770765878","https://openalex.org/W1880262756","https://openalex.org/W1970516435","https://openalex.org/W1982754133","https://openalex.org/W1995836673","https://openalex.org/W2000949542","https://openalex.org/W2006454222","https://openalex.org/W2044758663","https://openalex.org/W2046684509","https://openalex.org/W2054421446","https://openalex.org/W2061273963","https://openalex.org/W2065974896","https://openalex.org/W2083093865","https://openalex.org/W2084174584","https://openalex.org/W2086681524","https://openalex.org/W2092861621","https://openalex.org/W2098062695","https://openalex.org/W2107103981","https://openalex.org/W2107789863","https://openalex.org/W2108156076","https://openalex.org/W2114995492","https://openalex.org/W2115098571","https://openalex.org/W2116064496","https://openalex.org/W2116790685","https://openalex.org/W2125449110","https://openalex.org/W2128221272","https://openalex.org/W2130325614","https://openalex.org/W2130483595","https://openalex.org/W2133660688","https://openalex.org/W2145433315","https://openalex.org/W2158379031","https://openalex.org/W2159162038","https://openalex.org/W2163922914","https://openalex.org/W2165884492","https://openalex.org/W2209646292","https://openalex.org/W2215815030","https://openalex.org/W2285072859","https://openalex.org/W2405994726","https://openalex.org/W2435072835","https://openalex.org/W3022302886","https://openalex.org/W4293108385"],"related_works":["https://openalex.org/W6922558404","https://openalex.org/W7165060160","https://openalex.org/W2551534905","https://openalex.org/W7214561780","https://openalex.org/W2785925437","https://openalex.org/W4402223760","https://openalex.org/W6941358400","https://openalex.org/W7117848187","https://openalex.org/W7160598399","https://openalex.org/W7161760458"],"abstract_inverted_index":{"Risk":[0],"models":[1,17,247,272],"are":[2,18],"used":[3,80],"to":[4,23,58,122,140,152,200,284],"estimate":[5],"a":[6,114,186,234,263,290],"patient\u2019s":[7,115],"risk":[8,82,89,154,206,222,246,269],"of":[9,27,32,40,47,52,70,78,87,95,162,172,214,229,237,245,251,276,294],"suffering":[10],"particular":[11],"outcomes":[12,61],"throughout":[13],"clinical":[14,81,125,134,188,221],"practice.":[15,126],"These":[16],"important":[19],"for":[20,29,35,204,232,266],"matching":[21],"patients":[22,184],"the":[24,38,50,56,85,93,108,120,145,160,170,227,243,249,274],"appropriate":[25],"level":[26],"treatment,":[28],"effective":[30,97,104],"allocation":[31],"resources,":[33],"and":[34,45,62,91,105,168,190,270,279],"fairly":[36],"evaluating":[37],"performance":[39],"healthcare":[41,64],"providers.":[42],"The":[43],"application":[44],"development":[46,244],"methods":[48,139,194],"from":[49,156,185],"field":[51],"machine":[53],"learning":[54,153,233,268],"has":[55,119],"potential":[57,121],"improve":[59,124,211],"patient":[60,71],"reduce":[63],"spending":[65],"with":[66],"more":[67,96,103],"accurate":[68],"estimates":[69],"risk.":[72],"This":[73,147],"dissertation":[74,148],"addresses":[75],"several":[76,150],"limitations":[77],"currently":[79],"models,":[83,167],"through":[84,92,159,169],"identification":[86,171],"novel":[88,264],"factors":[90,155],"training":[94],"models.":[98],"As":[99],"wearable":[100],"monitors":[101],"become":[102],"less":[106],"costly,":[107],"previously":[109],"untapped":[110],"predictive":[111,173],"information":[112],"in":[113,144,248,257,273],"physiology":[116],"over":[117],"time":[118],"greatly":[123],"However":[127],"translating":[128],"these":[129,181,193],"technological":[130],"advances":[131],"into":[132],"real-world":[133,258],"impacts":[135],"will":[136],"require":[137],"computational":[138],"identify":[141],"high-risk":[142],"structure":[143],"data.":[146],"presents":[149],"approaches":[151,182,199,286],"physiological":[157],"recordings,":[158],"discovery":[161],"latent":[163],"states":[164],"using":[165,175],"topic":[166],"features":[174],"convolutional":[176],"neural":[177],"networks.":[178],"We":[179,224,261],"evaluate":[180],"on":[183,289],"large":[187,291],"trial":[189],"find":[191,280],"that":[192,209],"not":[195],"only":[196],"outperform":[197],"prior":[198],"leveraging":[201],"heart":[202],"rate":[203],"cardiac":[205,215],"stratification,":[207],"but":[208],"they":[210],"overall":[212],"prediction":[213],"death":[216],"when":[217,287],"considered":[218],"alongside":[219],"standard":[220,285],"factors.":[223],"also":[225],"demonstrate":[226],"utility":[228],"this":[230],"work":[231],"richer":[235],"description":[236],"sleep":[238],"recordings.":[239],"Additionally,":[240],"we":[241],"consider":[242],"presence":[250,275],"missing":[252,277],"data,":[253,278],"which":[254],"is":[255],"ubiquitous":[256],"medical":[259],"settings.":[260],"present":[262],"method":[265],"jointly":[267],"imputation":[271],"significant":[281],"improvements":[282],"relative":[283],"evaluated":[288],"national":[292],"registry":[293],"trauma":[295],"patients.":[296]},"counts_by_year":[{"year":2017,"cited_by_count":1}],"updated_date":"2026-11-04T10:54:05.112534","created_date":"2018-06-01T00:00:00"}
