Skip directly to search Skip directly to A to Z list Skip directly to navigation Skip directly to page options Skip directly to site content

Precision Health Database


Genomics & Health Impact Scan Database|Search|PHGKB

Last data update: Mar 25, 2023. (Total: 58925 Documents since 2012)
   dataset      
Filtered By:

Previous    

Records 1 - 26 (of 26 Records)

Query Trace:
A Comprehensive Review of the Role of Artificial Intelligence in Obstetrics and Gynecology.
Sagar N Malani et al. Cureus 2023 15(2) e34891
Associations of Preterm Birth with Dental and Gastrointestinal Diseases: Machine Learning Analysis Using National Health Insurance Data.
In-Seok Song et al. International journal of environmental research and public health 2023 20(3)
Interpretable machine learning to identify important predictors of birth weight: A prospective cohort study.
Liu Zheng et al. Frontiers in pediatrics 2022 10899954
Reporting and risk of bias of prediction models based on machine learning methods in preterm birth: A systematic review.
Yang Qiuyu et al. Acta obstetricia et gynecologica Scandinavica 2022
Bedside tracking of functional autonomic age in preterm infants.
Iyer Kartik K et al. Pediatric research 2022
Development of prognostic model for preterm birth using machine learning in a population-based cohort of Western Australia births between 1980 and 2015.
Wong Kingsley et al. Scientific reports 2022 12(1) 19153
The Prediction of Preterm Birth Using Time-Series Technology-Based Machine Learning: Retrospective Cohort Study.
Zhang Yichao et al. JMIR medical informatics 2022 10(6) e33835
Machine Learning Prediction Models for Neurodevelopmental Outcome After Preterm Birth: A Scoping Review and New Machine Learning Evaluation Framework.
van Boven Menne R et al. Pediatrics 2022
Associations between ambient temperature and risk of preterm birth in Sweden: A comparison of analytical approaches.
de Bont Jeroen et al. Environmental research 2022 113586
Prediction of preterm birth using artificial intelligence: a systematic review.
Akazawa Munetoshi et al. Journal of obstetrics and gynaecology : the journal of the Institute of Obstetrics and Gynaecology 2022 1-7
Predicting perinatal health outcomes using smartphone-based digital phenotyping and machine learning in a prospective Swedish cohort (Mom2B): study protocol.
Bilal Ayesha M et al. BMJ open 2022 12(4) e059033
Machine Learning-Based Prediction Model of Preterm Birth Using Electronic Health Record.
Sun Qi et al. Journal of healthcare engineering 2022 20229635526
Machine Learning Approach for Preterm Birth Prediction Using Health Records: Systematic Review.
Sharifi-Heris Zahra et al. JMIR medical informatics 2022 10(4) e33875
Asymmetric Three-dimensional Convolutions For Preterm Infants' Pose Estimation.
Migliorelli Lucia et al. Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference 2021 20213021-3024
Perinatal health predictors using artificial intelligence: A review.
Ramakrishnan Rema et al. Women's health (London, England) 2021 1717455065211046132
Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in South Asia and sub-Saharan Africa.
Sazawal Sunil et al. BMC pregnancy and childbirth 2021 21(1) 609
Using AMANHI-ACT cohorts for external validation of Iowa new-born metabolic profiles based models for postnatal gestational age estimation.
Sazawal Sunil et al. Journal of global health 2021 1104044
Development and validation of a risk prediction model of preterm birth for women with preterm labour symptoms (the QUIDS study): A prospective cohort study and individual participant data meta-analysis.
Stock Sarah J et al. PLoS medicine 2021 18(7) e1003686
An annotated data set for identifying women reporting adverse pregnancy outcomes on Twitter.
Klein Ari Z et al. Data in brief 2020 Oct 32106249
Analysis of big data for prediction of provider-initiated preterm birth and spontaneous premature deliveries and ranking the predictive features.
Khatibi Toktam et al. Archives of gynecology and obstetrics 2019 300(6) 1565-1582
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.
Fung Russell et al. The Lancet. Digital health 2020 Jul 2(7) e368-e375
Automatically Identifying Comparator Groups on Twitter for Digital Epidemiology of Pregnancy Outcomes.
Klein Ari Z et al. AMIA Joint Summits on Translational Science proceedings. AMIA Joint Summits on Translational Science 2020 2020317-325
Predicting motor outcome in preterm infants from very early brain diffusion MRI using a deep learning convolutional neural network (CNN) model.
Saha Susmita et al. NeuroImage 2020 Apr 116807
Artificial Intelligence: A New Paradigm in Obstetrics and Gynecology Research and Clinical Practice.
Iftikhar Pulwasha et al. Cureus 2020 Feb 12(2) e7124
Machine learning as a strategy to account for dietary synergy: an illustration based on dietary intake and adverse pregnancy outcomes.
Bodnar Lisa M et al. The American journal of clinical nutrition 2020 Feb
Deep Learning Predicts Extreme Preterm Birth from Electronic Health Records.
Gao Cheng et al. Journal of biomedical informatics 2019 Oct 103334
TOP