Not so weak-PICO: Leveraging weak supervision for Participants, Interventions, and Outcomes recognition for systematic review automation

Objective: PICO (Participants, Interventions, Comparators, Outcomes) analysis is vital but time-consuming for conducting systematic reviews (SRs). Supervised machine learning can help fully automate it, but a lack of large annotated corpora limits the quality of automated PICO recognition systems. The largest currently available PICO corpus is manually annotated, which is an approach that is often too expensive for the scientific community to apply. Depending on the specific SR question, PICO criteria are extended to PICOC (C-Context), PICOT (T-timeframe), and PIBOSO (B-Background, S-Study design, O-Other) meaning the static hand-labelled corpora need to undergo costly re-annotation as per the downstream requirements. We aim to test the feasibility of designing a weak supervision system to extract these entities without hand-labelled data.
Methodology: We decompose PICO spans into its constituent entities and re-purpose multiple medical and non-medical ontologies and expert-generated ru..., This upload contains four main zip files.

ds_cto_dict.zip: This zip file contains the four distant supervision dictionaries (P: participant.txt, I = intervention.txt, intervetion_syn.txt, O: outcome.txt) generated from clinicaltrials.gov using the Methodology described in Distant-CTO (https://aclanthology.org/2022.bionlp-1.34/). These dictionaries were used to create distant supervision labelling functions as described in the Labelling sources subsection of the Methodology. The data was derived from https://clinicaltrials.gov/

handcrafted_dictionaries.zip: This zip folder contains three files 1) gender_sexuality.txt: a list of possible genders and sexual orientations found across the web. The list needs to be more comprehensive. 2) endpoints_dict.txt: contains outcome names and the names of questionnaires used to measure outcomes assembled from PROM questionnaires and PROMs. and 3) comparator_dict: contains a list of idiosyncratic comparator terms like a sham, saline, placebo, etc.,..., All the datasets could be opened using text editors or Google sheets. The .zip files in the dataset can be opened using the archive utility on Mac OS and unzip functionality in Linux. (All Windows and Apple operating systems support the use of ZIP files without additional third-party software)