Collaborating Authors


AI-powered RPM can help address the rural neonatal care crisis


As hospital consolidation continues nationwide, rural areas are beginning to take a new shape – and it is not a pretty picture. According to a recent study from Health Affairs, newly acquired rural hospitals are eliminating surgical care services and mental health treatment access, despite a sharp rise in depression, suicide and addiction in the hard-hit rural communities. Even more stunning, these newly acquired hospitals are more likely to eliminate maternity and neonatal care than those that remain independent. Coupled with a worsening nursing shortage, this is a huge problem for rural American families. Even before the pandemic, maternal and infant outcomes in the U.S. were shockingly poor.

Using Kernel Methods and Model Selection for Prediction of Preterm Birth Machine Learning

We describe an application of machine learning to the problem of predicting preterm birth. We conduct a secondary analysis on a clinical trial dataset collected by the National In- stitute of Child Health and Human Development (NICHD) while focusing our attention on predicting different classes of preterm birth. We compare three approaches for deriving predictive models: a support vector machine (SVM) approach with linear and non-linear kernels, logistic regression with different model selection along with a model based on decision rules prescribed by physician experts for prediction of preterm birth. Our approach highlights the pre-processing methods applied to handle the inherent dynamics, noise and gaps in the data and describe techniques used to handle skewed class distributions. Empirical experiments demonstrate significant improvement in predicting preterm birth compared to past work.