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Predicting disease progress with imprecise lab test results
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DOI：10.1016/j.artmed.2022.102373.png)
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Abstract
En 中文
Clinical lab tests play an important role in disease diagnose and medical treatment, the test results have been utilized widely in predictive modeling tasks in healthcare. However, in most existing works, the loss function implicitly assumes that the value of the sample used to be predicted is the only correct one. This assumption fails to hold for lab test data, which usually are within respective tolerable ranges or imprecision ranges. In addition, the historical lab test data is always organized based on their sequential position, the timestamps between the data are often neglected. In this paper, we study the issue of building robust models while simultaneously taking imprecision and timestamp of the data into account with better generalization. In particular, IR lossis proposed in which each data in imprecision range space has a certain probability to be the real value, participating in the loss calculation. The loss is then defined as the integral of the error of each point in the impression range space. The sampling and discretization methods are proposed for loss calculation. A heuristic learning algorithm is developed to learn the model parameters. We further apply IR loss for disease progress prediction while the input data is organized as sequence. We reformulate the prediction task with timestamp based on Long Short-Term Memory (LSTM) network. At the same time, the timestamp is readily combined with the proposed IR loss to avoid the change of predicted result caused by the change of the test values in small time range. We conducted the experiments based on two real world datasets. Experimental results show that the prediction method based on IR loss can provide more accurate prediction result for different kinds of task and diverse learning methods. Our method can also provide more stable and consistent results when test samples are generated from imprecision range and small time range.
Keywords:
Prediction
Neural networks
Imprecise data
Health care
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