{'fraction_non_alphanumeric': 0.009884678747940691, 'fraction_numerical': 0.0, 'mean_word_length': 4.682242990654205, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Identifying Diagnostic Test Accuracy Publications using a Deep Model

In this work, we used a deep model architecture to identify DTA studies pertaining to a given review topic. We were provided the list of relevant documents selected based on abstracts and full text for different reviews topics. We extracted the abstract and title to be used as features to describe those documents, and learned the deep neural net model that takes as input the abstract and title of the studies, and topic of the review to obtain a binary classification of whether that study is a relevant DTA to the review in question.