Unlocking the Power of Deep PICO Extraction: Step-wise Medical NER Identification
This work addresses a specific bottleneck in medical literature analysis for researchers and clinicians, offering incremental improvements in PICO extraction.
The paper tackled the problem of extracting multiple evidence items within a single sentence in medical PICO extraction by proposing a step-wise method combining disease Named Entity Recognition (DNER) and PICO identification, achieving high performance and fine-grained results compared to conventional approaches.
The PICO framework (Population, Intervention, Comparison, and Outcome) is usually used to formulate evidence in the medical domain. The major task of PICO extraction is to extract sentences from medical literature and classify them into each class. However, in most circumstances, there will be more than one evidences in an extracted sentence even it has been categorized to a certain class. In order to address this problem, we propose a step-wise disease Named Entity Recognition (DNER) extraction and PICO identification method. With our method, sentences in paper title and abstract are first classified into different classes of PICO, and medical entities are then identified and classified into P and O. Different kinds of deep learning frameworks are used and experimental results show that our method will achieve high performance and fine-grained extraction results comparing with conventional PICO extraction works.