Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings
This work addresses the need for interpretable patient segmentation in medical practice, though it appears incremental as it builds on existing NLP methods with a new dataset.
The authors tackled the problem of clustering medical visits based on free-text descriptions to validate if similar conditions lead to similar diagnoses, achieving stable and separated segments positively validated against diagnoses using a corpus of 100,000 visits.
Is it true that patients with similar conditions get similar diagnoses? In this paper we show NLP methods and a unique corpus of documents to validate this claim. We (1) introduce a method for representation of medical visits based on free-text descriptions recorded by doctors, (2) introduce a new method for clustering of patients' visits and (3) present an~application of the proposed method on a corpus of 100,000 visits. With the proposed method we obtained stable and separated segments of visits which were positively validated against final medical diagnoses. We show how the presented algorithm may be used to aid doctors during their practice.