On the Representation of Machine Learning Results for Delirium Prediction in a Hospital Information System in Routine Care
Sai Pavan Kumar Veeranki, Dieter Hayn, Alphons Eggerth, Stefanie Jauk, Diether Kramer, Leodolter Werner, Günter Schreier · Studies in health technology and informatics · 2018
Digitalisation of health care for the purpose of medical documentation lead to huge amounts of data, hence having an opportunity to derive knowledge and associations of different attributes recorded. Many health care events can be prevented when identified. Machine learning algorithms could identify such events but there is ambiguity in understanding the suggestions especially in clinical setup. In this paper we are presenting how we explain the decision based on random forest to health care professionals in the course of the project predicting delirium during hospitalisation on the day of admission.