Not so weak PICO: leveraging weak supervision for participants, interventions, and outcomes recognition for systematic review automation.

ABSTRACT:

Objective

The aim of this study was to test the feasibility of PICO (participants, interventions, comparators, outcomes) entity extraction using weak supervision and natural language processing.

Methodology

We re-purpose more than 127 medical and nonmedical ontologies and expert-generated rules to obtain multiple noisy labels for PICO entities in the evidence-based medicine (EBM)-PICO corpus. These noisy labels are aggregated using simple majority voting and generative modeling to get consensus labels. The resulting probabilistic labels are used as weak signals to train a weakly supervised (WS) discriminative model and observe performance changes. We explore mistakes in the EBM-PICO that could have led to inaccurate evaluation of previous automation methods.

Results

I

SUBMITTER: Dhrangadhariya A 

PROVIDER: S-EPMC9828146 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature