# Interpretable Segmentation of Medical Free-Text Records Based on Word Embeddings

## TL;DR

This paper presents a novel NLP-based approach for representing and clustering medical visit records from free-text descriptions, resulting in stable patient segments validated against diagnoses, aiding clinical decision-making.

## Contribution

It introduces new methods for representing medical visits and clustering patient records based on word embeddings, validated on a large corpus of 100,000 visits.

## Key findings

- Stable and separated patient segments were identified.
- Segments correlated positively with final diagnoses.
- Method can assist doctors in clinical practice.

## Abstract

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.