  In existing deep learning methods, almost all loss functions assume that
sample data values used to be predicted are the only correct ones. This
assumption does not hold for laboratory test data. Test results are often
within tolerable or imprecision ranges, with all values in the ranges
acceptable. By considering imprecision samples, we propose an imprecision range
loss (IR loss) method and incorporate it into Long Short Term Memory (LSTM)
model for disease progress prediction. In this method, each sample in
imprecision range space has a certain probability to be the real value,
participating in the loss calculation. The loss is defined as the integral of
the error of each point in the impression range space. A sampling method for
imprecision space is formulated. The continuous imprecision space is
discretized, and a sequence of imprecise data sets are obtained, which is
convenient for gradient descent learning. A heuristic learning algorithm is
developed to learn the model parameters based on the imprecise data sets.
Experimental results on real data show that the prediction method based on IR
loss can provide more stable and consistent prediction result when test samples
are generated from imprecision range.
