Learning Patient Representations from Text
This addresses the challenge of improving patient phenotyping for applications like outcome prediction and clinical trials, though it is incremental as it builds on existing representation learning methods.
The paper tackled the problem of phenotyping in electronic health records by learning patient representations from text, achieving state-of-the-art performance on a standard comorbidity detection task.
Mining electronic health records for patients who satisfy a set of predefined criteria is known in medical informatics as phenotyping. Phenotyping has numerous applications such as outcome prediction, clinical trial recruitment, and retrospective studies. Supervised machine learning for phenotyping typically relies on sparse patient representations such as bag-of-words. We consider an alternative that involves learning patient representations. We develop a neural network model for learning patient representations and show that the learned representations are general enough to obtain state-of-the-art performance on a standard comorbidity detection task.