Generation and evaluation of artificial mental health records for Natural Language Processing.

ABSTRACT: A serious obstacle to the development of Natural Language Processing (NLP) methods in the clinical domain is the accessibility of textual data. The mental health domain is particularly challenging, partly because clinical documentation relies heavily on free text that is difficult to de-identify completely. This problem could be tackled by using artificial medical data. In this work, we present an approach to generate artificial clinical documents. We apply this approach to discharge summaries from a large mental healthcare provider and discharge summaries from an intensive care unit. We perform an extensive intrinsic evaluation where we (1) apply several measures of text preservation; (2) measure how much the model memorises training data; and (3) estimate clinical validity of the generat

SUBMITTER: Ive J 

PROVIDER: S-EPMC7224173 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature