Automatic evidence quality prediction to support evidence-based decision making
We present a classification model for the automatic quality grading of clinical evidence. We propose NLP-based approaches for extraction of informative features from text. We present a supervised learning approach using SVM classifiers for evidence grading. We show that the performance of our approach is comparable to human performance. Our quality grading approach can significantly reduce practitioners time needs.
automatic text classification automatic medical evidence classification decision support system medical natural language processing evidence-based medicine