Selective Prediction With Long Short-term Memory Using Unit-Wise Batch Standardization for Time Series Health Data Sets: Algorithm Development and Validation (Preprint)

Abstract

BACKGROUND In any healthcare system, both the classification of data and the confidence level of the classification are important. A selective prediction model is therefore needed to classify time-series health data according to confidence levels of prediction. OBJECTIVE The aim of this study is to develop a method using Long short-term memory (LSTM) models with reject option for time-series health data classification. METHODS To implement a reject option of classification output in LSTM models, an existing selective prediction method was adopted. However, a conventional selection function approach to LSTM does not achieve acceptable performance at the learning stage. To tackle this problem, we propose unit-wise batch standardization (UBS), which attempts to normalize each hidden unit in LSTM to reflect the structural characteristics of LSTM with respect to selection function. RESULTS From the results, the ability of our method to approximate the target confidence level was compared by coverage violations for two time series health datasets consisting of human activity and arrhythmia. For both datasets, our approach yielded lower average coverage violations (0.98% and 1.79% for each dataset) than conventional approach. In addition, the classification performance using the reject option was compared with other normalization methods. Our method demonstrates superior performance with respect to selective risk (12.63% and 17.82% for each dataset), false-positive rates (2.09% and 5.80% for each dataset), and false-negative rates (10.58% and 17.24% for each dataset). CONCLUSIONS We conclude that our normalization approach can help make selective predictions for time-series health data. We expect this technique will give users more confidence in classification systems and improve collaborative efforts between human and artificial intelligence levels in the medical field through the use of classification that reflects confidence.