Exploring methods to understand cancer disparities using natural language processing of clinical notes

Abstract:

Purpose/Objective(s) There is an unmet need to understand drivers of cancer disparities, such as social determinants of health (SDOH) and implicit bias. But these data are largely locked as free text in the clinical narrative and not directly analyzable, and there are no validated methods to automatically extract these data. We explore natural language processing (NLP) methods to extract and identity factors that may underlie disparities. Materials/Methods Our cohort consisted of 861 patients treated with radiotherapy for a thoracic malignancy from 2014-2020. Clinic notes created during patients’ radiotherapy course were collected and preprocessed; punctuation, digits, stop words, and person names removed (spaCy), and lists of lemmatized words created to compare word distributions across patient race. Disease/chemical entities (‘medical words’) were identified using the scispCy en_ner_bc5cdr_md model. Log …