CCS Explorer: Relevance Prediction, Extractive Summarization, and Named Entity Recognition from Clinical Cohort Studies

by   Irfan Al-Hussaini, et al.

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 relevance prediction using BioBERT and achieves an average Micro F1-Score of 77.8 CCS Explorer can reliably extract relevant information to summarize articles, saving time by ∼660×.


page 1

page 5

page 7


BioNerFlair: biomedical named entity recognition using flair embedding and sequence tagger

Motivation: The proliferation of Biomedical research articles has made t...

CamemBERT-bio: a Tasty French Language Model Better for your Health

Clinical data in hospitals are increasingly accessible for research thro...

Named entity recognition in resumes

Named entity recognition (NER) is used to extract information from vario...

A Biomedical Pipeline to Detect Clinical and Non-Clinical Named Entities

There are a few challenges related to the task of biomedical named entit...

Neural language models for text classification in evidence-based medicine

The COVID-19 has brought about a significant challenge to the whole of h...

EasyNER: A Customizable Easy-to-Use Pipeline for Deep Learning- and Dictionary-based Named Entity Recognition from Medical Text

Medical research generates a large number of publications with the PubMe...

Automatically Summarizing Evidence from Clinical Trials: A Prototype Highlighting Current Challenges

We present TrialsSummarizer, a system that aims to automatically summari...

Please sign up or login with your details

Forgot password? Click here to reset