An interpretable mortality prediction model for COVID-19 patients - alternative approach

The pandemic spread of coronavirus leads to increased burden on healthcare services worldwide. Experience shows that required medical treatment can reach limits at local clinics and fast and secure clinical assessment of the disease severity becomes vital. In [1] a model is presented for predicting the mortality of COVID-19 patients from their biomarkers. Three biomarkers have been selected by ranking with a supervised Multi-tree XGBoost classifier. The prediction model is built up as a binary decision tree with depth three and achieves AUC scores of up to 97.84{+/-}0.37 and 95.06{+/-} 2.21 for training and external test data sets, resp. In human assessment and decision making influencing parameters usually arent considered as sharp numbers but rather as Fuzzy terms [2], and inferencing primarily yields Fuzzy terms or continuous grades rather than binary decisions. Therefore, I examined a Sugenotype Fuzzy classifier [3] for disease assessment and decision support. In addition, I used an artificial neural network (SOM, [4]) for selecting the biomarkers. Modelling and validation was done with the identical data base provided by [1]. With the complete training and test data sets, the Fuzzy prediction model achieves improved AUC scores of up to 98.59 or 95.12 The improvements with the Fuzzy classifier obviously become clear as physicians can interpret output grades to belong to positive or negative class more or less strongly. An extension of the Fuzzy model, which takes into account the trend in key features over time, provides excellent results with the training data, which, however, could not be finally verified due to the lack of suitable test data. The generation and training of the Fuzzy models was fully automatic and without additional adjustment with the help of ANFIS from Matlab(C).