A tree is a very common datastructure. I stumbled upon this subject because of a requirement that is needed for a mathematical solution. This solution needs to keep track of changes that start with a single change but can have multiple effects. Each effect, i.e. change, can result in turn into multiple changes, hence the need of a tree structure.
Software development for Informedica has been a one man show. Maybe that’s about to change. However, this means that the development process has to change as well. In this blog I propose a development process to enable this change. The fork and pull model will be used to cooperate.
Machine Learning (ML) (or Artificial Intelligence AI) is trending. Publications with regard to ML are on the increase in medical literature.
Specifically critical care medicine generates huge amounts of detailed data. In a recent article steps are described to enable use of ML in daily clinical practice. This blog will describes an actual working implementation of the first step in ML to be used in clinical practice.
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.