Background: There is a trend in healthcare for developing models for predictions of disease to enable early intervention and improve outcome.
Instrument: We present the use of artificial intelligence algorithms that were developed by Gynisus Ltd. using mathematical algorithms.
Experience: Data were retrospectively collected on pregnant women that delivered at a single institution. Hundreds of parameters were collected and found to have different importance and correlation with the likelihood to develop gestational diabetes mellitus (GDM). We highlight 3 of 29 specific parameters that were important in pregestation and in early pregnancy, which have not been previously correlated with GDM.
Conclusion: This predictive tool identified parameters that are not currently being used as predictors in GDM, even before pregnancy. This tool opens the possibility of intervening on patients identified at risk for GDM and its complications. Future prospective studies are needed.
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| http://dx.doi.org/10.1177/1932296820948883 | DOI Listing |
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