Xiaowen-JI/Semi-automation-of-systematic-review-of-clinical-trials-in-medical-psychology-with-BERT-models
We employed pre-trained BERT models (distillBERT, BioBert, and SciBert) for text-classifications of the titles and abstracts of clinical trials in medical psychology. The average score of AUC is 0.92. A stacked model was then built by featuring the probability predicted by distillBERT and keywords of search domains. The AUC improved to 0.96 with F1, precision, and recall increasing to 0.95, 0.94, and 0.96 respectively. Training sample size of 100 results in the most cost-effective performance.
This tool helps researchers in medical psychology quickly identify relevant clinical trials from large databases like PubMed. It takes in titles and abstracts of clinical trial studies and classifies them based on relevance. The output is a list of studies that are highly likely to be pertinent to your systematic review, significantly speeding up the initial screening process for medical psychology researchers.
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Use this if you are conducting a systematic review of clinical trials in medical psychology and need to efficiently filter through thousands of study abstracts.
Not ideal if your research domain is outside of medical psychology or if you require 100% human-level accuracy for every single initial screening decision.
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Aug 18, 2021
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