학술논문

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study
Document Type
Article
Author
Fung, RussellVillar, JoseDashti, AliIsmail, Leila CheikhStaines-Urias, EleonoraOhuma, Eric OSalomon, Laurent JVictora, Cesar GBarros, Fernando CLambert, AnnCarvalho, MariaJaffer, Yasmin ANoble, J AlisonGravett, Michael GPurwar, ManoramaPang, RuyanBertino, EnricoMunim, ShamaMin, Aung MyatMcGready, RoseNorris, Shane ABhutta, Zulfiqar AKennedy, Stephen HPapageorghiou, Aris TOurmazd, AbbasNorris, SAbbott, SEAbubakar, AAcedo, JAhmed, IAl-Aamri, FAl-Abduwani, JAl-Abri, JAlam, DAlbernaz, EAlgren, HAl-Habsi, FAlija, MAl-Jabri, HAl-Lawatiya, HAl-Rashidiya, BAltman, DGAl-Zadjali, WKAndersen, HFAranzeta, LAsh, SBaricco, MBarros, FCBarsosio, HBatiuk, CBatra, MBerkley, JBertino, EBhan, MKBhat, BABhutta, ZABlakey, IBornemeier, SBradman, ABuckle, MBurnham, OBurton, FCapp, ACararra, VICarew, RCarrara, VICarter, AACarvalho, MChamberlain, PCheikh, Ismail LCheikh Ismail, LChoudhary, AChoudhary, SChumlea, WCCondon, CCorra, LACosgrove, CCraik, Rda Silveira, MFDanelon, Dde Wet, Tde Leon, EDeshmukh, SDeutsch, GDhami, JDi, Nicola PDighe, MDolk, HDomingues, MDongaonkar, DEnquobahrie, DEskenazi, BFarhi, FFernandes, MFinkton, DFonseca, SFrederick, IOFrigerio, MGaglioti, PGarza, CGilli, GGilli, PGiolito, MGiuliani, FGolding, JGravett, MGGu, SHGuman, YHe, YPHoch, LHussein, SIbanez, DIoannou, CJacinta, NJackson, NJaffer, YAJaiswal, SJimenez-Bustos, JMJuangco, FRJuodvirsiene, LKatz, MKemp, BKennedy, SKetkar, MKhedikar, VKihara, MKilonzo, JKisiang'ani, CKizidio, JKnight, CLKnight, HEKunnawar, NLaister, ALambert, ALanger, ALephoto, TLeston, ALewis, TLiu, HLloyd, SLumbiganon, PMacauley, SMaggiora, EMahorkar, CMainwaring, MMalgas, LMatijasevich, AMcCormick, KMcGready, RMiller, RMin, AMitidieri, AMkrtychyan, VMonyepote, BMota, DMulik, IMunim, SMuninzwa, DMusee, NMwakio, SMwangudzah, HNapolitano, RNewton, CRNgami, VNoble, JANorris, SNorris, TNosten, FOas, KOberto, MOcchi, LOchieng, ROhuma, EOOlearo, EOlivera, IOwende, MGPace, CPan, YPang, RYPapageorghiou, ATPatel, BPaul, VPaulsene, WPuglia, FPurwar, MRajan, VRaza, AReade, DRivera, JRocco, DARoseman, FRoseman, SRossi, CRothwell, PMRovelli, ISaboo, KSalam, RSalim, MSalomon, LSanchez, Luna MSande, JSarris, ISavini, SSclowitz, IKSeale, AShah, JSharps, MShembekar, CShen, YJShorten, MSignorile, FSingh, ASohoni, SSomani, ASorensen, TKSoria- Frisch, AStaines Urias, EStein, AStones, WTaori, VTayade, KTodros, TUauy, RVaralda, AVenkataraman, MVictora, CVillar, JVinayak, SWaller, SWalusuna, LWang, JHWang, LWanyonyi, SWeatherall, DWiladphaingern, SWilkinson, AWilson, DWu, MHWu, QQWulff, KYellappan, DYuan, YZaidi, SZainab, GZhang, JJZhang, Y
Source
The Lancet Digital Health; July 2020, Vol. 2 Issue: 7 pe368-e375, 8p
Subject
Language
ISSN
25897500
Abstract
Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18–36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth.