SVM in Data Mining of EHRs Brad Lipson

PUBLISHED August 22, 2023

Introduction

Support Vector Machines (SVM) are a great way to mine data in Electronic Medical Records (EMR). You can use them to find patterns in the data that might be hard to find with regular statistical methods. SVMs can also be used to make models that can use new data to make accurate predictions. It is important to keep in mind, though, that SVMs can be hard to train, especially on big datasets. SVMs can also be responsive to how the SVM kernel and hyperparameters are chosen. Once the SVM model has been trained, it can be used to guess what will happen with new data. For example, a model could be used to figure out how likely it is that a patient will get a certain illness or what will happen to a patient who already has that disease. Before you can use SVMs for data mining in EMRs, you need to prepare the data since the noise and outliers should be taken out of the data. It is also important to feature engineer the data, which will help make new features that may be more useful for the SVM model. SVMs can be used to get useful information from the data and to make models that can improve the care of patients. Here are some more reasons why using SVMs for data mining in EMRs is helpful in the clinical setting.  SVMs can determine a model to predict which patients might survive a severe illness in the hospital. This is important for data mining in EMRs, where the data is often complicated since SVMs can handle noise and errors well. This can assist in predicting those patients at risk for mortality in the Intensive Care Unit (ICU).