Abstract
Early detection of breast cancer (BC) is crucial in determining patient outcomes. Modeling the patient journey prior to BC diagnosis is therefore an important task. Patient diagnoses are often available as free text, and difficult to represent for predictive analytics. We introduce the use of sentence transformers, paired alongside a novel association through unsupervised clustering to yield highly relevant patient journey representations.
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