The core of evidence-based medicine is to read and analyze numerous papers in the medical literature on a specific clinical problem and summarize the authoritative answers to that problem. Currently, to formulate a clear and focused clinical problem, the popular PICO framework is usually adopted, in which each clinical problem is considered to consist of four parts: patient/problem (P), intervention (I), comparison (C) and outcome (O). In this study, we compared several classification models that are commonly used in traditional machine learning. Next, we developed a multitask classification model based on a soft-margin SVM with a specialized feature engineering method that combines 1-2gram analysis with TF-IDF analysis. Finally, we trained and tested several generic models on an open-source data set from BioNLP 2018. The results show that the proposed multitask SVM classification model based on 1-2gram TF-IDF features exhibits the best performance among the tested models.
@article{arxiv.1901.08351,
title = {Extracting PICO elements from RCT abstracts using 1-2gram analysis and multitask classification},
author = {Xia Yuan and Liao xiaoli and Li Shilei and Shi Qinwen and Wu Jinfa and Li Ke},
journal= {arXiv preprint arXiv:1901.08351},
year = {2019}
}