一种新型且精准的预测妊娠期糖尿病的分析工具
A Novel and Precise Profiling Tool to Predict Gestational Diabetes.
机构信息
Division of Maternal-Fetal Medicine, Department of Obstetrics and Gynecology, Maimonides Medical Center, Brooklyn, NY, USA.
Gynisus Ltd., Santa Monica, CA, USA.
出版信息
J Diabetes Sci Technol. 2021 Jul;15(4):891-896. doi: 10.1177/1932296820948883. Epub 2020 Aug 13.
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.
背景
医疗保健领域有一种趋势，即开发疾病预测模型，以实现早期干预和改善预后。
仪器
我们介绍了 Gynisus 有限公司使用数学算法开发的人工智能算法的应用。
经验
数据是回顾性地收集在单一机构分娩的孕妇。收集了数百个参数，并发现它们与发生妊娠期糖尿病（GDM）的可能性具有不同的重要性和相关性。我们重点介绍了 29 个特定参数中的 3 个，这些参数在妊娠前和孕早期很重要，但以前与 GDM 没有相关性。
结论
该预测工具确定了目前未用作 GDM 预测因子的参数，甚至在怀孕前也是如此。该工具为识别有 GDM 及其并发症风险的患者提供了干预的可能性。需要进行未来的前瞻性研究。
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