Clinical Cohort Studies (CCS), such as randomized clinical trials, are a
great source of documented clinical research. Ideally, a clinical expert
inspects these articles for exploratory analysis ranging from drug discovery
for evaluating the efficacy of existing drugs in tackling emerging diseases to
the first test of newly developed drugs. However, more than 100 articles are
published daily on a single prevalent disease like COVID-19 in PubMed. As a
result, it can take days for a physician to find articles and extract relevant
information. Can we develop a system to sift through the long list of these
articles faster and document the crucial takeaways from each of these articles?
In this work, we propose CCS Explorer, an end-to-end system for relevance
prediction of sentences, extractive summarization, and patient, outcome, and
intervention entity detection from CCS. CCS Explorer is packaged in a web-based
graphical user interface where the user can provide any disease name. CCS
Explorer then extracts and aggregates all relevant information from articles on
PubMed based on the results of an automatically generated query produced on the
back-end. For each task, CCS Explorer fine-tunes pre-trained language
representation models based on transformers with additional layers. The models
are evaluated using two publicly available datasets. CCS Explorer obtains a
recall of 80.2%, AUC-ROC of 0.843, and an accuracy of 88.3% on sentence
relevance prediction using BioBERT and achieves an average Micro F1-Score of
77.8% on Patient, Intervention, Outcome detection (PIO) using PubMedBERT. Thus,
CCS Explorer can reliably extract relevant information to summarize articles,
saving time by $\sim \text{660}\times$.

CCS Explorer 是一个端到端的系统，可从 CCS 中提取相关信息以总结文章，以生物 BERT 为基础的句子相关性预测可达到 80.2％召回率和 0.843 的 AUC-ROC，以 PubMedBERT 为基础的患者，干预措施，结果检测的平均微 F1 分数为 77.8％，可节省近 660 倍的时间。