In a previous post, a setup using an F# script to perform machine learning with Microsoft.ML is described. A very import aspect for a successful model is picking the right features that will predict the label, i.e. the exposure that is associated with outcome. In this post a simple F# feature selection algorithm is described that automatically figures out which features result in the ‘best’ model.
Machine learning is the new kid on the block and has become accessible with the advent of the Microsoft.ML library. In medicine machine learning hasn’t been used that much. Most scientific epidemiological research still relies on established statistical analysis. The core principle, however, is the same. Known exposure is used to predict outcome. In Pediatric Intensive Care, prediction of mortality is used to benchmark and monitor performance. In the Netherlands, for this purpose, data is gathered at a national basis. This blog will discuss a machine learning setup to analyze data using a data set with 13.793 PICU admissions.