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Journal Article
Ultrasound in Obstetrics & Gynecology, ISSN 0960-7692, 09/2017, Volume 50, Issue 3, pp. 373 - 382
Journal Article
PLoS ONE, ISSN 1932-6203, 07/2017, Volume 12, Issue 7, p. e0181468
Background Late-onset preeclampsia is the most prevalent phenotype of this syndrome; nevertheless, only a few biomarkers for its early diagnosis have been... 
UTERINE ARTERY DOPPLER | NORMAL-PREGNANCY | HYPERTENSIVE DISORDERS | MULTIDISCIPLINARY SCIENCES | PLACENTAL GROWTH-FACTOR | ANTI-ANGIOGENIC FACTORS | SOLUBLE ENDOGLIN | FACTOR RECEPTOR-1 | MATERNAL PLASMA-CONCENTRATIONS | NECROSIS-FACTOR-ALPHA | FOR-GESTATIONAL-AGE | Biomarkers - metabolism | Prospective Studies | Humans | Risk Factors | Placenta Growth Factor - blood | Vascular Endothelial Growth Factor A - blood | Placenta Growth Factor - metabolism | Pregnancy Proteins - metabolism | Vascular Endothelial Growth Factor A - metabolism | Biomarkers - blood | Gestational Age | Pre-Eclampsia - etiology | Case-Control Studies | Pregnancy | Pre-Eclampsia - blood | Female | Signal Transduction - physiology | Pre-Eclampsia - metabolism | Proteomics - methods | Longitudinal Studies | Proteome - metabolism | Proteases | Preeclampsia | Genetic aspects | Diagnosis | Biological markers | Identification and classification | Health aspects | Animal models | Control methods | Childrens health | Performance prediction | Gestation | Matrix metalloproteinase | Gestational age | Proteins | Signal transduction | Prediction models | Mathematical models | Bioindicators | Metalloproteinase | Vascular endothelial growth factor | Age | Gynecology | Abundance | Pre-eclampsia | Patients | Obstetrics | Biological activity | Medicine | Sensitivity | Placenta | Correlation analysis | Proteomics | Aptamers | Biomarkers | Diagnostic systems | Matrilysin
Journal Article
Journal Article
Gastroenterology, ISSN 0016-5085, 2017, Volume 152, Issue 4, pp. 840 - 850.e3
Journal Article
by Jhee, JH and Lee, S and Park, Y and Lee, SE and Kim, YA and Kang, SW and Kwon, JY and Park, JT
PLOS ONE, ISSN 1932-6203, 08/2019, Volume 14, Issue 8, p. e0221202
Preeclampsia is one of the leading causes of maternal and fetal morbidity and mortality. Due to the lack of effective preventive measures, its prediction is... 
VARIABILITY | PATHOGENESIS | PATTERN-RECOGNITION | MULTIDISCIPLINARY SCIENCES | HEART-FAILURE | OUTCOMES | LEVEL | KIDNEY | ASSOCIATION | ARTIFICIAL NEURAL-NETWORKS | PREGNANCY | Pre-Eclampsia - epidemiology | Pregnancy Trimester, Second | Humans | Logistic Models | Support Vector Machine | Gestational Age | Machine Learning | Pre-Eclampsia - physiopathology | Pregnancy Trimester, Third | Pregnancy | Age of Onset | Bayes Theorem | Adult | Female | Pre-Eclampsia - diagnosis | Cluster analysis | Diabetic retinopathy | Statistical methods | Family medical history | Glucose | Learning | Prediction models | Classification | Blood pressure | Life sciences | Decision trees | Pattern analysis | Fetuses | Gynecology | Regression analysis | Pattern recognition | Prenatal care | Urea | Learning algorithms | Hospitals | Surveillance | Biomarkers | Bayesian analysis | Electronic health records | Calcium | Laboratories | Gestation | Electronic medical records | Variables | Regression models | Calcium (blood) | Mathematical models | Age | Creatinine | Urine | Hypertension | Calcium (urinary) | Statistical analysis | Pre-eclampsia | Preventive medicine | Obstetrics | Cholesterol | Morbidity | White blood cells | Support vector machines | Algorithms | Womens health | Collaboration | Machine learning | Diabetes | Stochasticity | Potassium | Artificial intelligence | Medical electronics
Journal Article
Ultrasound in Obstetrics and Gynecology, ISSN 0960-7692, 09/2009, Volume 34, Issue 3, pp. 274 - 282
Journal Article