Objectives Extraction of PICO (Populations, Interventions, Comparison, and
Outcomes) entities is fundamental to evidence retrieval. We present a novel
method PICOX to extract overlapping PICO entities.
Materials and Methods PICOX first identifies entities by assessing whether a
word marks the beginning or conclusion of an entity. Then it uses a multi-label
classifier to assign one or more PICO labels to a span candidate. PICOX was
evaluated using one of the best-performing baselines, EBM-NLP, and three more
datasets, i.e., PICO-Corpus, and RCT publications on Alzheimer's Disease or
COVID-19, using entity-level precision, recall, and F1 scores.
Results PICOX achieved superior precision, recall, and F1 scores across the
board, with the micro F1 score improving from 45.05 to 50.87 (p << 0.01). On
the PICO-Corpus, PICOX obtained higher recall and F1 scores than the baseline
and improved the micro recall score from 56.66 to 67.33. On the COVID-19
dataset, PICOX also outperformed the baseline and improved the micro F1 score
from 77.10 to 80.32. On the AD dataset, PICOX demonstrated comparable F1 scores
with higher precision when compared to the baseline.
Conclusion PICOX excels in identifying overlapping entities and consistently
surpasses a leading baseline across multiple datasets. Ablation studies reveal
that its data augmentation strategy effectively minimizes false positives and
improves precision.

PICOX 通过识别开始和结束实体的单词，并使用多标签分类器将一个或多个 PICO 标签分配给跨度候选项，从而优于基线方法，在多个数据集上表现出更高的精确度、召回率和 F1 得分，并通过数据增强策略有效地减少了误报，提高了精确度。