Skip to main content

Table 3 AUC-ROC of COVID-19 mortality prediction results by using baselines

From: Predicting COVID-19 disease progression and patient outcomes based on temporal deep learning

  0 days earlya 3 days earlya 6 days early 9 days early 12 days early
Coxb 0.955 ± 0.06 0.992 ± 0.02 0.870 ±0.01 0.85 ±0.01 0.810 ±0.01
k-NNc 0.950 ± 0.02 0.909 ± 0.01 0.890 ±0.02 0.840 ±0.02 0.816 ±0.01
SVMd 0.969 ± 0.04 0.954 ± 0.02 0.930 ± 0.03 0.895 ±0.04 0.857 ±0.02
DTe 0.974 ±0.01 0.959 ± 0.03 0.924 ±0.00 0.897 ±0.01 0.869 ±0.03
BPNNf 0.980 ±0.02 0.954 ±0.05 0.933 ±0.0 1 0.894 ±0.02 0.878 ±0.03
PNNg 0.985 ±0.01 0.961 ±0.02 0.940 ±0.0 2 0.889 ±0.02 0.889 ±0.02
RNNh 0.985 ±0.01 0.960 ±0.01 0.931 ±0.00 0.910 ±0.02 0.871 ±0.01
LSTMi 0.990 ±0.01 0.961 ±0.02 0.937 ±0.02 0.920 ±0.03 0.897 ±0.03
T-LSTMj 0.997 ± 0.00 0.969 ± 0.01 0.947 ±0.03 0.921 ±0.03 0.914 ±0.02
  1. a n days early: The models make prediction n days before the final death/survival time. They use sequence data from day 0 to n days before the last time to predict
  2. b Cox: Cox’s proportional hazards regression model is semi parametric regression model. It can analyze the influence of many factors on outcomes. It is used in [19]
  3. c k-NN: k-Nearest Neighbors method makes prediction based on the information of nearest k samples in training set. In this mortality prediction task, the most accurate results appeared when k = 3
  4. d SVM: Support Vector Machines classify by solving the separation hyperplane which can divide the training data correctly and has the largest geometric interval
  5. e DT: Decision tree is a simple classifier consisting of sequences of hierarchically organized binary decisions. It is used in [33]
  6. f BPNN: Back Propagation Neuron Network makes the signal and the error propagate forward and backward separately. It is used in [20]
  7. g PNN: Probabilistic Neural Network is a forward propagation network and does not need back propagation to optimize parameters by using Bayesian decision-making. It is used in [21]
  8. h RNN: Recurrent Neural Network have been introduced in the ‘T-LSTM’ section
  9. i LSTM: Long Short-Term Memory which we have introduced in the ‘T-LSTM’ section. Here, the hyperparameter setting is same as T-LSTM
  10. j T-LSTM: Time-aware LSTM is the model used in this paper. Its inputs are the three-dimensional vectors and the time intervals. The values for each dimension are the values of LDH, lymphocyte and hs-CRP in patients’ blood tests. Its output is the binary result 0/1. Here, 0 indicates survival and 1 indicates death. The hidden states in its units are 64 dimensional, and the fully connected layer has 32 dimensions