Pipelined Ensemble Architecture for Mortality Prediction on MIMIC III

Abstract

Automated healthcare decision support has seen a huge rise with the improved data collection models in hospitals and also improved machine learning based techniques that exhibit high possibilities for automation. Automating healthcare systems as an aide for clinical practitioners can ensure fast and more accurate results for the patients and can also aid in hospital administration. Mortality prediction has been one of the major and critical factors that determines the type of treatment and the level of resources that has to be allocated for a patient. This work presents a pipelined ensemble architecture that can be used for effective prediction of mortality levels of a patient. The pipeline model has been designed in multiple levels to ensure improvement of quality of the medical data and effective prediction. The pipelined architecture model has been compared with existing state-of-the-art model, and the results indicate high performance with 92% accuracy levels, ensuring the model is suitable for use in real time mortality prediction.