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Fig. 2 | BMC Medical Informatics and Decision Making

Fig. 2

From: Improving reference prioritisation with PICO recognition

Fig. 2

PICO recognition and abstract screening process. In the first phase, the PICO recognition model is trained to predict the PICO mention spans on a human annotated corpus of abstracts. In the second phase, a collection of abstracts is processed by the PICO recognition model and the results along with the original abstract are used to create a vector representation of each abstract. In the final phase, a user labels abstracts as being included (relevant) or excluded, these decisions are used to train a machine learning (ML) model that uses the vector representation. The ML model is applied to the remaining unlabelled abstracts, which are then sorted by their predicted relevancy, the user sees the top ranked abstracts, labels them, and this process repeats

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