Models for predicting preterm birth have historically focused on babies considered very preterm, born at 28 to 32 weeks, or moderate to late preterm, born at 32 to 37 weeks. Only a few studies have looked at those born extremely preterm, before 28 weeks of development, yet these early fetuses account for the vast majority of newborn deaths.
Aware that knowledge saves lives, Vanderbilt University Medical Center bioinformatics specialist You Chen, is leading an initiative to provide clinicians with predictive tools for extreme preterm birth (EPB).
In a study published in the Journal of Biomedical Informatics, Chen and colleagues (including DBMI's Brad Malin and Digna Velez-Edwards) identified five top risk factors in EPB that yielded high predictive accuracy. For this work, he employed a recurrent neural network (RNN), a machine learning approach that combines predictions from multiple models.
“We found that we could predict preterm birth at 20 weeks gestational age with high accuracy,” Chen said. “This is eight weeks earlier than before.”
Now, he is training and refining this model to yield even higher and more widely applicable predictive results. Read more in Discover.