Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol

about

Machine learning from fetal flow waveforms to predict adverse perinatal outcomes: a study protocol

Item

http://www.wikidata.org/entity/Q58027735

description

scientific article published on 12 February 2018

@en

wetenschappelijk artikel

@nl

наукова стаття, опублікована в лютому 2018

@uk

name

Machine learning from fetal fl ...... tal outcomes: a study protocol

@en

Machine learning from fetal fl ...... tal outcomes: a study protocol

@nl

type

Item

label

Machine learning from fetal fl ...... tal outcomes: a study protocol

@en

Machine learning from fetal fl ...... tal outcomes: a study protocol

@nl

prefLabel

Machine learning from fetal fl ...... tal outcomes: a study protocol

@en

Machine learning from fetal fl ...... tal outcomes: a study protocol

@nl

P1476

Machine learning from fetal fl ...... tal outcomes: a study protocol

@en

P2093

Babar Hasan

http://www.w3.org/2001/XMLSchema#string

Bart Bijnens

http://www.w3.org/2001/XMLSchema#string

Devyani Chowdhury

http://www.w3.org/2001/XMLSchema#string

P2860

National, regional, and worldwide estimates of stillbirth rates in 2015, with trends from 2000: a systematic analysis.

Maternal and newborn outcomes in Pakistan compared to other low and middle income countries in the Global Network's Maternal Newborn Health Registry: an active, community-based, pregnancy surveillance mechanism.

A computational model of the fetal circulation to quantify blood redistribution in intrauterine growth restriction.

Neonatal cause-of-death estimates for the early and late neonatal periods for 194 countries: 2000-2013.

First-trimester uterine artery Doppler and adverse pregnancy outcome: a meta-analysis involving 55,974 women.

Introducing a Mobile-Connected Umbilical Doppler Device (UmbiFlow™) into a Primary Care Maternity Setting: Does This Reduce Unnecessary Referrals to Specialised Care? Results of a Pilot Study in Kraaifontein, South Africa.

Intrauterine growth restriction: new concepts in antenatal surveillance, diagnosis, and management.

Fetal and umbilical Doppler ultrasound in high-risk pregnancies.

Characterization of myocardial motion patterns by unsupervised multiple kernel learning.

Patient-specific estimates of vascular and placental properties in growth-restricted fetuses based on a model of the fetal circulation.

P304

http://www.w3.org/2001/XMLSchema#string

P31

scholarly article

P356

10.12688/GATESOPENRES.12796.1

http://www.w3.org/2001/XMLSchema#string

P407

English

P478

http://www.w3.org/2001/XMLSchema#string

P50

Zahra Hoodbhoy

Fyezah Jehan

P577

2018-02-12T00:00:00Z

http://www.w3.org/2001/XMLSchema#dateTime

P698

29863146

http://www.w3.org/2001/XMLSchema#string

P921

machine learning

P932

5974597

http://www.w3.org/2001/XMLSchema#string