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Machine learning-based multi-label medical text classifications can be usedto enhance the understanding of the human body and aid the need for patientcare. We present a broad study on clinical natural language processingtechniques to maximise a feature representing text when predicting medicalcodes on patients with multi-morbidity. We present results of multi-labelmedical text classification problems with 18, 50 and 155 labels. We compareseveral variations to embeddings, text tagging, and pre-processing. Forimbalanced data we show that labels which occur infrequently, benefit the mostfrom additional features incorporated in embeddings. We also show that highdimensional embeddings pre-trained using health-related data present asignificant improvement in a multi-label setting, similarly to the way theyimprove performance for binary classification. High dimensional embeddings fromthis research are made available for public use.

Seeing The Whole Patient: Using Multi-Label Medical Text Classification Techniques to Enhance Predictions of Medical Codes