MedLens: Improve Mortality Prediction Via Medical Signs Selecting and Regression
This work addresses data-quality issues in mortality prediction for healthcare, but it is incremental as it focuses on improving existing methods through data selection and interpolation.
The paper tackled the problem of high missing rates and irrelevant medical signs in EHR data for mortality prediction, and achieved a high accuracy of 0.96 AUC-ROC and 0.81 AUC-PR, exceeding previous benchmarks.
Monitoring the health status of patients and predicting mortality in advance is vital for providing patients with timely care and treatment. Massive medical signs in electronic health records (EHR) are fitted into advanced machine learning models to make predictions. However, the data-quality problem of original clinical signs is less discussed in the literature. Based on an in-depth measurement of the missing rate and correlation score across various medical signs and a large amount of patient hospital admission records, we discovered the comprehensive missing rate is extremely high, and a large number of useless signs could hurt the performance of prediction models. Then we concluded that only improving data-quality could improve the baseline accuracy of different prediction algorithms. We designed MEDLENS, with an automatic vital medical signs selection approach via statistics and a flexible interpolation approach for high missing rate time series. After augmenting the data-quality of original medical signs, MEDLENS applies ensemble classifiers to boost the accuracy and reduce the computation overhead at the same time. It achieves a very high accuracy performance of 0.96 AUC-ROC and 0.81 AUC-PR, which exceeds the previous benchmark.