{'fraction_non_alphanumeric': 0.02403846153846154, 'fraction_numerical': 0.0, 'mean_word_length': 5.4051282051282055, '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}}
Transforming Unstructured Clinical Free-Text Corpora into Reconfigurable Medical Digital Collections

In this paper, we describe how to transform unstructured free-text clinical corpora, made from reports written in natural language and complementary assets (e.g., medical images, laboratory results, etc.), into collections of digital objects compatible with Clavy, a tool for managing reconfigurable digital collections. It will allow healthcare experts to subsequently reorganize the resulting collections to adapt them to their specific needs. The transformation will be achieved through the use of MetaMap, a robust tool for mapping clinical texts into the UMLS (Unified Medical Language System) thesaurus. Thus, by processing reports with MetaMap, we will be able to extract a significant set of corpus-specific UMLS terms, grouped according to relevant semantic types, which will be used to support a preliminary organization of the resources in the Clavy collection. We illustrate the viability of the approach with the generation of a reconfigurable Clavy collection from the Indiana Chest X-ray corpus of radiology reports and images. On the basis of this case study, we also discuss the strengths and weaknesses of the approach proposed.