{"id":"https://openalex.org/W2889001085","doi":"https://doi.org/10.2196/11766","title":"Predicting Obstetric Disease With Machine Learning Applied to Patient-Reported Data","display_name":"Predicting Obstetric Disease With Machine Learning Applied to Patient-Reported Data","publication_year":2018,"publication_date":"2018-08-29","ids":{"openalex":"https://openalex.org/W2889001085","doi":"https://doi.org/10.2196/11766","mag":"2889001085"},"language":"en","primary_location":{"id":"doi:10.2196/11766","doi":"https://doi.org/10.2196/11766","is_oa":true,"landing_page_url":"https://doi.org/10.2196/11766","pdf_url":"https://www.iproc.org/2018/2/e11766/PDF","source":{"id":"https://openalex.org/S4210193617","display_name":"Iproceedings","issn_l":"2369-6893","issn":["2369-6893"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iproceedings","raw_type":"journal-article"},"volume":null,"type":"article","is_traditional":true,"indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"diamond","oa_url":"https://www.iproc.org/2018/2/e11766/PDF","any_repository_has_fulltext":null},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062036807","display_name":"Danielle E. Bradley","orcid":"https://orcid.org/0000-0002-4010-9401","observed_orcids":["https://orcid.org/0000-0002-4010-9401"]},"institutions":[],"countries":[],"is_corresponding":true,"raw_author_name":"Danielle Bradley","raw_affiliation_strings":["Ovia Health 76 Summer Street Boston, MA, United States","Ovia Health, Boston, MA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ovia Health 76 Summer Street Boston, MA, United States","institution_ids":[]},{"raw_affiliation_string":"Ovia Health, Boston, MA, United States","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5070307315","display_name":"ERIN O. LANDAU","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Erin Landau","raw_affiliation_strings":["Ovia Health, Boston, MA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ovia Health, Boston, MA, United States","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5034578066","display_name":"Adam Wolfberg","orcid":"https://orcid.org/0000-0002-5717-8702","observed_orcids":["https://orcid.org/0000-0002-5717-8702"]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Adam Wolfberg","raw_affiliation_strings":["Ovia Health, Boston, MA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ovia Health, Boston, MA, United States","institution_ids":[]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5016460992","display_name":"Alex Baron","orcid":null,"observed_orcids":[]},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Alex Baron","raw_affiliation_strings":["Ovia Health, Boston, MA, United States"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Ovia Health, Boston, MA, United States","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":["https://openalex.org/A5062036807"],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":0.2499,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.59940172,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":88,"max":93},"biblio":{"volume":"4","issue":"2","first_page":"e11766","last_page":"e11766"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8589000105857849,"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"}},"topics":[{"id":"https://openalex.org/T11396","display_name":"Artificial Intelligence in Healthcare","score":0.8589000105857849,"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/T11446","display_name":"Mobile Health and mHealth Applications","score":0.06710000336170197,"subfield":{"id":"https://openalex.org/subfields/3600","display_name":"General Health Professions"},"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/T10290","display_name":"Pregnancy and preeclampsia studies","score":0.053199999034404755,"subfield":{"id":"https://openalex.org/subfields/2729","display_name":"Obstetrics and Gynecology"},"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/patient-reported-data","display_name":"patient-reported data","score":0.8600000143051147},{"id":"https://openalex.org/keywords/mobile-health-apps","display_name":"mobile health apps","score":0.9390000104904175},{"id":"https://openalex.org/keywords/preeclampsia","display_name":"preeclampsia","score":0.9449999928474426},{"id":"https://openalex.org/keywords/gestational-diabetes-mellitus","display_name":"gestational diabetes mellitus","score":0.9990000128746033},{"id":"https://openalex.org/keywords/preterm-birth","display_name":"preterm birth","score":0.9639999866485596},{"id":"https://openalex.org/keywords/gradient-boosted-decision-trees","display_name":"gradient boosting decision tree","score":0.7630000114440918},{"id":"https://openalex.org/keywords/menstrual-cycle-tracking","display_name":"menstrual cycle tracking","score":0.7379999756813049}],"concepts":[{"id":"https://openalex.org/C2988145974","wikidata":"https://www.wikidata.org/wiki/Q620615","display_name":"Mobile apps","level":2,"score":0.5395507216453552},{"id":"https://openalex.org/C2779134260","wikidata":"https://www.wikidata.org/wiki/Q12136","display_name":"Disease","level":2,"score":0.527023196220398},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.5268346667289734},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.49767377972602844},{"id":"https://openalex.org/C2780433410","wikidata":"https://www.wikidata.org/wiki/Q5276090","display_name":"Digital health","level":3,"score":0.42717689275741577},{"id":"https://openalex.org/C3018822202","wikidata":"https://www.wikidata.org/wiki/Q1324077","display_name":"Patient data","level":2,"score":0.4168073236942291},{"id":"https://openalex.org/C71924100","wikidata":"https://www.wikidata.org/wiki/Q11190","display_name":"Medicine","level":0,"score":0.41336315870285034},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3968908190727234},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.362425833940506},{"id":"https://openalex.org/C108827166","wikidata":"https://www.wikidata.org/wiki/Q175975","display_name":"Internet privacy","level":1,"score":0.3511772155761719},{"id":"https://openalex.org/C509550671","wikidata":"https://www.wikidata.org/wiki/Q126945","display_name":"Medical education","level":1,"score":0.3211742639541626},{"id":"https://openalex.org/C160735492","wikidata":"https://www.wikidata.org/wiki/Q31207","display_name":"Health care","level":2,"score":0.28673434257507324},{"id":"https://openalex.org/C136764020","wikidata":"https://www.wikidata.org/wiki/Q466","display_name":"World Wide Web","level":1,"score":0.2672921419143677},{"id":"https://openalex.org/C142724271","wikidata":"https://www.wikidata.org/wiki/Q7208","display_name":"Pathology","level":1,"score":0.10613253712654114},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.2196/11766","doi":"https://doi.org/10.2196/11766","is_oa":true,"landing_page_url":"https://doi.org/10.2196/11766","pdf_url":"https://www.iproc.org/2018/2/e11766/PDF","source":{"id":"https://openalex.org/S4210193617","display_name":"Iproceedings","issn_l":"2369-6893","issn":["2369-6893"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iproceedings","raw_type":"journal-article"},{"id":"doi:10.2196/preprints.11766","doi":"https://doi.org/10.2196/preprints.11766","is_oa":true,"landing_page_url":"https://doi.org/10.2196/preprints.11766","pdf_url":null,"source":null,"license":"cc-by-sa","license_id":"https://openalex.org/licenses/cc-by-sa","version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"posted-content"}],"best_oa_location":{"id":"doi:10.2196/11766","doi":"https://doi.org/10.2196/11766","is_oa":true,"landing_page_url":"https://doi.org/10.2196/11766","pdf_url":"https://www.iproc.org/2018/2/e11766/PDF","source":{"id":"https://openalex.org/S4210193617","display_name":"Iproceedings","issn_l":"2369-6893","issn":["2369-6893"],"is_oa":true,"is_in_doaj":false,"is_core":false,"listed_in":[],"host_organization":null,"host_organization_name":null,"host_organization_lineage":[],"host_organization_lineage_names":[],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Iproceedings","raw_type":"journal-article"},"sustainable_development_goals":[{"display_name":"Good health and well-being","score":1.0,"id":"https://metadata.un.org/sdg/3"}],"study_designs":[],"awards":[],"funders":[],"has_content":{"grobid_xml":true,"pdf":true},"content_urls":{"pdf":"https://content.openalex.org/works/W2889001085.pdf","grobid_xml":"https://content.openalex.org/works/W2889001085.grobid-xml"},"referenced_works_count":0,"referenced_works":[],"related_works":["https://openalex.org/W2944375542","https://openalex.org/W2944092577","https://openalex.org/W6906296924","https://openalex.org/W7154161313","https://openalex.org/W3010721756","https://openalex.org/W4405944975","https://openalex.org/W4281703125","https://openalex.org/W4406981006","https://openalex.org/W4408358859","https://openalex.org/W4210510546"],"abstract_inverted_index":{"Background:":[0],"The":[1,169],"rise":[2],"of":[3,20,151,166,180,204,227,247,305,314],"highly":[4],"engaging":[5],"digital":[6],"health":[7,94,97,279,283,328],"mobile":[8,73],"apps":[9,74],"over":[10],"the":[11,26,51,149,164,181,186,189,202,225,228,245,248,303,312],"past":[12],"few":[13],"years":[14],"has":[15],"created":[16,146],"repositories":[17],"containing":[18],"billions":[19],"patient-reported":[21,263],"data":[22,40,136,264],"points":[23,83],"that":[24],"have":[25,68],"potential":[27],"to":[28,44,49,132,139,147,162,176,201,212,223,235,243,257,274,289,317],"inform":[29],"clinical":[30,193,276],"research":[31,286],"and":[32,59,79,126,135,156,282,301],"advance":[33],"medicine.":[34],"Objective:":[35],"To":[36],"determine":[37,290],"if":[38],"self-reported":[39],"could":[41,320],"be":[42,198],"leveraged":[43],"create":[45],"machine":[46,113],"learning":[47,107,114],"algorithms":[48,108],"predict":[50,266],"presence":[52,165],"of,":[53],"or":[54,104,196,251],"risk":[55,150],"for,":[56],"obstetric":[57,268],"outcomes":[58,297],"related":[60],"conditions.":[61],"Methods:":[62],"More":[63],"than":[64],"10":[65],"million":[66],"women":[67],"downloaded":[69],"Ovia":[70,77,80],"Health\u2019s":[71],"three":[72],"(Ovia":[75],"Fertility,":[76],"Pregnancy,":[78],"Parenting).":[81],"Data":[82],"logged":[84],"by":[85,278],"app":[86,141],"users":[87],"can":[88,265,295],"include":[89],"information":[90],"about":[91],"menstrual":[92],"cycle,":[93],"history,":[95],"current":[96],"status,":[98],"nutrition":[99],"habits,":[100],"exercise":[101],"activity,":[102],"symptoms,":[103],"moods.":[105],"Machine":[106],"were":[109,145],"developed":[110,125],"using":[111,128],"supervised":[112],"methodologies,":[115],"specifically,":[116],"Gradient":[117],"Boosting":[118],"Decision":[119],"Tree":[120],"algorithms.":[121],"Each":[122],"algorithm":[123,231,253],"was":[124,174,185,219,239],"trained":[127],"anywhere":[129],"from":[130,137,210,221,233,241,255],"385":[131],"5770":[133],"features":[134],"77,621":[138],"121,740":[140],"users.":[142],"Results:":[143],"Algorithms":[144,260],"detect":[148],"developing":[152],"preeclampsia,":[153],"gestational":[154],"diabetes,":[155],"preterm":[157],"delivery,":[158],"as":[159,161,183],"well":[160],"identify":[163],"existing":[167],"preeclampsia.":[168],"positive":[170,206],"predictive":[171],"value":[172],"(PPV)":[173],"set":[175],"0.75":[177,222,242],"for":[178,298],"all":[179,215],"models,":[182],"this":[184],"threshold":[187],"where":[188],"researchers":[190],"felt":[191],"a":[192,205],"response\u2014additional":[194],"screening":[195,277,306],"testing\u2014would":[197],"reasonable,":[199],"due":[200],"likelihood":[203],"outcome.":[207],"Sensitivity":[208],"ranged":[209],"24%":[211,234],"75%":[213],"across":[214],"models.":[216],"When":[217,237],"PPV":[218,238],"adjusted":[220,240],"0.52,":[224],"sensitivity":[226,246],"preeclampsia":[229,249],"prediction":[230],"rose":[232],"85%.":[236],"0.65,":[244],"detection":[250],"diagnostic":[252],"increased":[254],"37%":[256],"79%.":[258],"Conclusions:":[259],"based":[261],"on":[262],"serious":[267],"conditions":[269],"with":[270],"accuracy":[271],"levels":[272],"sufficient":[273],"guide":[275],"care":[280],"providers":[281],"plans.":[284],"Further":[285],"is":[287],"needed":[288],"whether":[291],"such":[292],"an":[293],"approach":[294],"improve":[296],"at-risk":[299],"patients":[300,318],"reduce":[302],"cost":[304],"those":[307],"not":[308],"at":[309],"risk.":[310],"Presenting":[311],"results":[313],"these":[315],"models":[316],"themselves":[319],"also":[321],"provide":[322],"important":[323],"insight":[324],"into":[325],"otherwise":[326],"unknown":[327],"risks.":[329]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-11-11T12:16:26.678958","created_date":"2018-09-07T00:00:00"}