A data-driven health index for neonatal morbidities
iScience, ISSN: 2589-0042, Vol: 25, Issue: 4, Page: 104143
2022
- 4Citations
- 21Captures
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Example: if you select the 1-year option for an article published in 2019 and a metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019. If you select the 3-year option for the same article published in 2019 and the metric category shows 90%, that means that the article or review is performing better than 90% of the other articles/reviews published in that journal in 2019, 2018 and 2017.
Citation Benchmarking is provided by Scopus and SciVal and is different from the metrics context provided by PlumX Metrics.
Metrics Details
- Citations4
- Citation Indexes4
- CrossRef2
- Captures21
- Readers21
- 21
Article Description
Whereas prematurity is a major cause of neonatal mortality, morbidity, and lifelong impairment, the degree of prematurity is usually defined by the gestational age (GA) at delivery rather than by neonatal morbidity. Here we propose a multi-task deep neural network model that simultaneously predicts twelve neonatal morbidities, as the basis for a new data-driven approach to define prematurity. Maternal demographics, medical history, obstetrical complications, and prenatal fetal findings were obtained from linked birth certificates and maternal/infant hospitalization records for 11,594,786 livebirths in California from 1991 to 2012. Overall, our model outperformed traditional models to assess prematurity which are based on GA and/or birthweight (area under the precision-recall curve was 0.326 for our model, 0.229 for GA, and 0.156 for small for GA). These findings highlight the potential of using machine learning techniques to predict multiple prematurity phenotypes and inform clinical decisions to prevent, diagnose and treat neonatal morbidities.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S2589004222004138; http://dx.doi.org/10.1016/j.isci.2022.104143; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85127482832&origin=inward; http://www.ncbi.nlm.nih.gov/pubmed/35402862; https://linkinghub.elsevier.com/retrieve/pii/S2589004222004138; https://dx.doi.org/10.1016/j.isci.2022.104143
Elsevier BV
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