Figure 3 . Performance of rwTTD prediction in homogeneous
population during cross-validation. a. Example terminated ratio curve at
0.0008 termination rate. b. Comparison between predicted curve and gold
standard curve by different base learners at different termination
rates. c. Cumulative error at different termination rates. d. Cumulative
error with different numbers of training examples. e. Cumulative error
with different numbers of predictive features. f. Cumulative error with
different feature noise levels.
With the increase of examples, there is a steady decrease in the percent
of error (Fig 3d, Fig. S1b, Fig. S3 ). This is expected as we
have more training examples, the inference of the overall curve is
improved. With 100 examples, the median error using cumulative errors
are 19.84%, 22.92%, 20.22% for ExtraTreeRegressor, Linear Regression,
and SVM respectively. In contrast, with 10,000 examples, the median
errors using cumulative error is 6.81%, 7.95%, 6.28% for
ExtraTreeRegressor, Linear Regression, and SVM, respectively. We
consider this is caused by more stable performance and inference of
parameters in models with more training examples. On the other hand, the
number of predictive features does not affect performance (Fig.
3e, Fig. S1c, Fig. S4 ). Additionally, with a sufficient number of
examples (5000), noise level on individual features does not affect
model performance (Fig. 3f, Fig. S1d ,Fig. S5 ). The above
results demonstrated the overall robust performance of the model when
the patients are derived from the same population.