Natural Language Processing to Automate Cerebrovascular Event Identification in Stroke Alerts.

ABSTRACT:

Background

Frequent false-positive stroke alerts can strain health resources. Machine learning models can predict stroke alert accuracy and potentially reduce this strain, but these models require time-consuming labeling of large data sets. Weak labeling can accelerate machine learning model development by assigning annotations based on expert-defined heuristic rules rather than manual review. We sought to label a large, unlabeled sample of stroke alerts according to a binary outcome (presence/absence of acute cerebrovascular disease) using weak labeling.

Methods

We developed a weak labeling heuristic ensemble consisting of 4 hierarchical tiers, each of which generated a binary label using custom labeling algorithms. Tier 1 used rule-based named-entity recognition to generat

SUBMITTER: Erekat AN 

PROVIDER: S-EPMC12697633 | biostudies-literature | 2025 Nov

REPOSITORIES: biostudies-literature