{'fraction_non_alphanumeric': 0.018900343642611683, 'fraction_numerical': 0.0, 'mean_word_length': 5.072916666666667, '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}}
Cohort Selection through Content-Based Image Retrieval: vfM A Case Study

In this paper, we propose ViewFinder Medicine (vfM) for automatically identifying cohort classes for MRI scans. It involves predicting a cohort class for the heretofore unseen patient (and related images) and offering linkages to historical diagnosis data associated with the members of the predicted cohort class. The basic idea is to offer a relatively accurate cohort class for a new patient so that the cohort can be used as a baseline to understand current patient's status and develop a treatment plan.