  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.
