Error detection in blood work : Acomparison of self-supervised deep learning-based models

Sammanfattning: Errors in medical testing may cause serious problems that has the potential to severely hurt patients. There are many machine learning methods to discover such errors. However, due to the rarity of errors, it is difficult to collect enough examples to learn from them. It is therefore important to focus on methods that do not require human labeling. This study presents a comparison of neural network-based models for the detection of analytical errors in blood tests containing five markers of cardiovascular health. The results show that error detection in blood tests using deep learning is a promising preventative mechanism. It is also shown that it is beneficial to take a multivariate approach to error detection so that the model examines several blood tests at once. There may also be benefits to looking at multiple health markers simultaneously, although this benefit is more pronounced when looking at individual blood tests. The comparison shows that a supervised approach significantly outperforms outlier detection methods on error detection. Given the effectiveness of the supervised model, there is reason to further study and potentially employ deep learning-based error detection to reduce the risk of errors.