Combination drug therapies are treatment regimens that involve two or more
drugs, administered more commonly for patients with cancer, HIV, malaria, or
tuberculosis. Currently there are over 350K articles in PubMed that use the
"combination drug therapy" MeSH heading with at least 10K articles published
per year over the past two decades. Extracting combination therapies from
scientific literature inherently constitutes an $n$-ary relation extraction
problem. Unlike in the general $n$-ary setting where $n$ is fixed (e.g.,
drug-gene-mutation relations where $n=3$), extracting combination therapies is
a special setting where $n \geq 2$ is dynamic, depending on each instance.
Recently, Tiktinsky et al. (NAACL 2022) introduced a first of its kind dataset,
CombDrugExt, for extracting such therapies from literature. Here, we use a
sequence-to-sequence style end-to-end extraction method to achieve an F1-Score
of $66.7\%$ on the CombDrugExt test set for positive (or effective)
combinations. This is an absolute $\approx 5\%$ F1-score improvement even over
the prior best relation classification score with spotted drug entities (hence,
not end-to-end). Thus our effort introduces a state-of-the-art first model for
end-to-end extraction that is already superior to the best prior non end-to-end
model for this task. Our model seamlessly extracts all drug entities and
relations in a single pass and is highly suitable for dynamic $n$-ary
extraction scenarios.

本论文基于 sequence-to-sequence style 的 end-to-end extraction 方法，以 CombDrugExt 数据集为基础，实现了普遍疾病中复方药物的关系提取，F1-Score 为 66.7%。