Learning from undercoded clinical records for automated International Classification of Diseases (ICD) coding

To develop an unbiased objective for learning automatic coding algorithms from clinical records annotated with only partial relevant International Classification of Diseases codes, as annotation noise in undercoded clinical records used as training data can mislead the learning process of deep neural networks.

We use Medical Information Mart for Intensive Care III as our dataset. We employ positive-unlabeled learning to achieve unbiased loss estimation, which is free of misleading training signal. We then utilize reweighting mechanism to compensate for the imbalance between positive and negative samples. To further close the performance gap caused by poor quality annotation, we integrate the supervision provided by the automatic annotation tool Medical Concept Annotation Toolkit which can ease the heavy burden of manual validation.

Our benchmarking results show that positive-unlabeled learning with reweighting outperforms competitive baseline methods over a range of missing label ratios. Integrating supervision provided by annotation tool further boosted the performance.

Considering the annotation noise and severe imbalance, unbiased loss estimation and reweighting mechanism are both important for learning from undercoded clinical records. Unbiased loss requires the estimation of false negative ratios and estimation through trained models is practical and competitive.

The combination of positive-unlabeled learning with reweighting and supervision provided by the annotation tool is a promising solution to learn from undercoded clinical records.