Revealing the impact of social circumstances on the selection of cancer therapy through natural language processing of social work notes.

ABSTRACT:

Objective

We aimed to investigate the impact of social circumstances on cancer therapy selection using natural language processing to derive insights from social worker documentation.

Materials and methods

We developed and employed a Bidirectional Encoder Representations from Transformers (BERT) based approach, using a hierarchical multi-step BERT model (BERT-MS), to predict the prescription of targeted cancer therapy to patients based solely on documentation by clinical social workers. Our corpus included free-text clinical social work notes, combined with medication prescription information, for all patients treated for breast cancer at UCSF between 2012 and 2021. We conducted a feature importance analysis to identify the specific social circumstances that impact cancer th

SUBMITTER: Sun S 

PROVIDER: S-EPMC11470153 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature