May 15, 2010 · In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts.

May 15, 2010 · In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical abstracts.

Feb 2, 2015 · In this study, we tested multiple supervised classification algorithms and their combinations for detecting PICO elements within medical ...

we have automatically gathered large training/testing data sets for each PICO element. ... promising results with an f-measure score of 86.3% for P, 67% for I and ...

Peer Review reports. From: Combining classifiers for robust PICO element detection. Original Submission. 7 Dec 2009, Submitted, Original manuscript. 7 Dec 2009 ...

Two sets of naive Bayes classifiers were developed for PICO detection. •. We trained one set with first sentences and the other with all sentences.

Combining classifiers for robust pico element detection. BMC medical informatics and decision making, 10(1):29, 2010. [2] Florian Boudin, Lixin Shi, and ...

Sep 5, 2023 · We propose a two-step NLP pipeline to extract PICO elements from RCT abstracts: (i) sentence classification using a prompt-based learning model and (ii) PICO ...

This work proposes a hybrid approach combining the robustness of MLMs and the fine grained level of RBMs to enhance PICO extraction process and facilitate ...

In this work, we propose a novel deep learning model for recognizing PICO elements in biomedical abstracts.