Evidence-based medicine is an approach whereby clinical decisions are supported by the best available findings gained from scien- tific research. This requires efficient access to such evidence. To this end, abstracts in evidence-based medicine can be labeled using a set of predefined medical categories, the so- called PICO criteria. This paper presents an approach to automatically annotate sentences in medical abstracts with these labels. Since both structural and sequential information are important for this classification task, we use kLog, a new language for statistical relational learning with kernels. Our results show a clear improvement with respect to state-of-the-art systems.

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