VentSR: A Self-Rectifying Deep Learning Method for Extubation Readiness Prediction

Abstract

Timely recognition of extubation readiness is critical, because prolonged and premature intubation will lead to sever complications and costs. Clinical assessment is time consuming and challenging and it has attracted increasing attention of machine learning in recent years. However, the data used for extubation predictions have the following flaws: 1) Manual recording errors and missing data; 2) Unreliable ventilation labels due to inadequate judgement from clinicians. Both may possibly lead to wrong ventilation labels, but existing machine learning methods for extubation prediction largely ignored this critical issue. In this paper, we proposed a self-rectifying deep learning method for extubation readiness prediction, called VentSR. It improves the prediction performance by a self-rectifying strategy, and the rectification is achieved through model training without clinical experience. To be detailed, VentSR firstly identifies possibly wrong samples by two components: Inconsistency between K-means and Labels (IKL) and Inconsistency between Model Predictions and Labels (IPL). IKL partitions a rough subset, and IPL iteratively refines this subset through training. Additionally, we designed Adjustment Operation to enhance IPL ability for refinement. Samples identified in this subset are rectified and used to train the model. The unrectified test set is directly fed into the trained model to obtain prediction results. Experiments demonstrate that VentSR outperforms other baselines. Further comparisons on high-confidence test set indicate that VentSR achieves 79.4 AUPRC, increasing by 26.0%. Feature importance analysis and case study illustration again reveals that VentSR are of potential practical usage of informing clinicians with accurate extubation readiness.