Predicting Disease Progress with Imprecise Lab Test Results
This addresses the challenge of handling imprecise medical data for healthcare applications, but it is incremental as it builds on existing LSTM methods with a novel loss function.
The authors tackled the problem of disease progress prediction using lab test data with inherent imprecision by proposing an imprecision range loss (IR loss) method integrated into an LSTM model, achieving more stable and consistent predictions on real data when test samples are generated from imprecision ranges.
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.