The electronic medical record (EMR) 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.
The good
Machine learning models have a dynamic nature. They can learn from new observations and improve their predictive accuracy as the size of the datasets increases. These models are particularly useful for managing multiple predictor variables with countless potential interactions, which may require more work to account for using traditional models.
The algorithms used in this branch of artificial intelligence incorporate predictor variables that may not be visible by mere background knowledge. Furthermore, when unsupervised machine learning methods are used, unknown patterns can be unveiled.
Another advantage of machine learning is that algorithms don’t make assumptions about the relationship between predictors and outcome variables (e.g. linear relationship). 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.
However, there are also drawbacks to consider when it comes to machine learning.
The bad and the ugly
One of the biggest challenges faced by machine learning models is interpretation and determining causality from evidence.
Physicians are typically well-versed in interpreting conventional statistics like odds and relative ratios. However, they may not have the same level of familiarity with the more complex statistics used in machine learning, such as in random forest models, where multiple decision trees are used to predict the classification of an outcome. Furthermore, as more predictors are added to the model, interpretability becomes even more difficult. To address this issue, methods using dimension reduction may help with interpretability at the expense of accuracy.
Finally, another potential pitfall of machine learning is overfitting, where the model becomes too reliant on the input data. This can be prevented by ensuring a proper balance between the size of the training data and the validation data.
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.
This type of analgesia has been associated with fetal heart rate changes. A significant drop in fetal heart rate may indicate potential issues with the baby’s health. However, several factors can increase the likelihood of a slow heart rate in the fetus, and it can be difficult for doctors to predict this outcome.
The multifactorial nature of fetal heart rate changes requires analyzing multiple possible predictor variables in a poorly understood medical problem. That is why our study utilized a machine learning approach to identify the important variables for our model.
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 (MSE) 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.
According to our research, certain factors such as the technique used for neuraxial analgesia (combined spinal-epidural), the amount of bupivacaine administered, the mother’s BMI, and the length of the initial stage of labor play a significant role in determining fetal heart rate changes following neuraxial analgesia during labor.
Our findings have practical implications for the medical field. They may increase the physician’s awareness of the potential risks for fetal heart rate drops in healthy pregnant patients and adjust their treatment plans accordingly. For example, if a patient has a high BMI, the physician can be extra cautious and avoid certain techniques or medications, such as a combined spinal-epidural technique or high doses of bupivacaine.
Key points to take home
Our article showcases how machine learning can assist in understanding medical problems that remain unclear. When applied correctly, machine learning is a valuable resource that can enhance healthcare procedures and improve patient care by using a greater amount of data from EMRs.
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