A hybrid model using LSTM and decision tree for mortality prediction and its application in provider performance evaluation

摘要

The risk adjusted mortality rate, which is also called standardized mortality ratio (SMR), is one widely used quality measure to evaluate healthcare provider performance. Logistic regression and decision tree are two traditional risk models for mortality rate calculation. Though some machine learning based approaches could achieve higher accuracy, they are hard to interpret and may have poor calibration scores. In this paper, we evaluated multiple machine learning approaches with different formats of longitudinal data, and proposed a hybrid approach based on long short-term memory (LSTM) model and decision tree. The new hybrid method provides a comparable area under the receiver operating characteristic curve (AUC) performance as LSTM with a better calibration score. Using a set of 3,473 patients with 10 months of data from historical, large scale ESRD patient data, the LSTM with long format data approach achieved AUC for prediction of mortality of 0.772 compared to 0.758 for logistic regression and 0.726 for a decision tree model. The hybrid approach could reach 0.783, a little higher than both LSTM and decision tree model. The hybrid approach has the best calibration performance based on the Hosmer Lemeshow test.