In a BMC_series blog post, the authors discuss the use of a machine learning approach to evaluate the important variables in predicting fetal heart rate changes after neuraxial analgesia in healthy pregnant patients, and its benefits and challenges.
© Dmitry Naumov / Stock.adobe.comThe electronic medical record as a big data source entails a large volume of data produced at a high speed. Hence, the complexity of the datasets it generates hinders analysis through traditional methods. Machine learning is an alternative to conventional data analysis that aids in understanding these large datasets.
Another advantage of machine learning is that algorithms don’t make assumptions about the relationship between predictors and outcome variables . Instead, they rely on data rather than human decisions to generate a model that closely explains the data’s behavior. This leads to improved accuracy of the models.The bad and the ugly One of the biggest challenges faced by machine learning models is interpretation and determining causality from evidence.
The future of fetal heart rate monitoring: a machine learning approach In our study, we used a machine learning approach to identify the important predictor variables to forecast changes in fetal heart rate after neuraxial analgesia during labor. We evaluated the predictive capabilities of four models – Principal Components Regression, Random Forest, Elastic Net Model, and Multiple Linear Regression – for fetal heart rate changes. Among them, the Random Forest model had the best performance, with a mean squared error of 0.9, while the other models had an MSE above 42. MSE is a measure of accuracy that represents the average difference between the predicted and measured values.
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