Automated extraction of post-stroke functional outcomes from unstructured electronic health records.

ABSTRACT:

Purpose

Population level tracking of post-stroke functional outcomes is critical to guide interventions that reduce the burden of stroke-related disability. However, functional outcomes are often missing or documented in unstructured notes. We developed a natural language processing (NLP) model that reads electronic health records (EHR) notes to automatically determine the modified Rankin Scale (mRS).

Method

We included consecutive patients (⩾18 years) with acute stroke admitted to our center (2015-2024). mRS scores were obtained from the Get With the Guidelines registry and clinical notes (if documented), and used as the gold standard to compare against NLP-generated scores. We used text-based features from notes, along with age, sex, discharge status, and outpatient follow

SUBMITTER: Fernandes M 

PROVIDER: S-EPMC11752148 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature