Using Structured Health Information for Controlled Generation of Clinical Cases in F rench (2024)

Abstract: Text generation opens up new prospects for overcoming the lack of open corpora in fields such as healthcare, where data sharing is bound by confidentiality. In this study, we compare the performance of encoder-decoder and decoder-only language models for the controlled generation of clinical cases in French. To do so, we fine-tuned several pre-trained models on French clinical cases for each architecture and generate clinical cases conditioned by patient demographic information (gender and age) and clinical features.Our results suggest that encoder-decoder models are easier to control than decoder-only models, but more costly to train.

## introduction the performance of current text generation models makes it difficult for humans to distinguish between natural and synthetic text @xcite , paving the way for a wide range of applications including data augmentation and addressing resource sparsity @xcite . in this article, we consider the case of reference documents that cannot be shared because of the personal information they contain but are sufficiently generic to mutualize processing resources on a community scale. one way of developing shared processes is to work with synthetic documents that are comparable in content and style to reference documents. we focus on electronic health records, though our methods can be applied to other fields with documentsharing constraints due to privacy. creating relevant synthetic documents is not trivial and must take several dimensions into account. as mentioned before, synthetic documents should be comparable to reference documents in terms of style, structure, and content, without leaking personal information that may be contained in the * these authors contributed equally to this work. the order is alphabetical. training corpora. while directly identifying information can be subject to robust upstream deidentification, this does not make documents anonymous according to the definition of the general data protection regulation (gdpr). indeed, deidentification, whether automatic or manual, does not prevent cross-referencing medical information, which can particularly impact privacy for rare diseases. it is possible to leverage the abilities of current text generation models to generate synthetic documents. however, such models are not as efficient when it comes to specialized domains such as the medical domain, even more so in languages other than english. thus, the ability to precisely control the generation process is important both for medical consistency and for preserving the privacy of the information contained in real texts. in this article, we propose a methodology for controlling text generation in terms of content. more specifically, the goal is to condition the generation of medical reports on patient profiles. following the example of work carried out on the generation of synthetic patient profiles in terms of structured data @xcite , these profiles take the form of a set of medical concepts. this approach, which is part of a data-to-text generation problem, has the advantage over a textual priming approach of being able to finely control the information used for conditioning. the latter is implemented by training a neural language model with a set of pairs, each composed of a patient profile in the form of concepts and a reference report corresponding to this profile. within this framework, the contributions of our paper are as follows: ## overall method as outlined in the introduction, we cast the task as a data-to-text generation problem where structured health data is used to shape the contents of synthetic text. of course, finding the conditioning data within the generated texts cannot be the only criterion for evaluating the models: they would only need to reproduce their input to be judged as perfect. this conditioning must therefore be close in nature to the reference documents we wish to emulate. as mentioned in section 2.1, this double conditioning can be achieved either by fine-tuning the language model used for generation with control elements or by steering the model during inference. we have opted for the former solution, as the latter implies applying elaborate text analysis processes during generation to check compliance with the conditioning, which is costly. the first solution, however, presupposes the availability of training data combining conditioning data and example texts conforming to this conditioning. to this end, we have adopted a strategy comparable to @xcite for story generation, taken over by @xcite for medical reports, and consisting in automatically extracting the conditioning data from the example texts. this strategy obviously presupposes the availability of text analysis tools capable of extracting this conditioning data from example texts with a sufficiently high level of performance. it therefore requires a close coupling between generation and analysis capabilities but eliminates the need for costly manual annotation. in the present case, we are focusing on medical concepts and are therefore dependent on models for extracting these concepts from medical reports but the genericity of this strategy means that new conditioning elements can easily be taken into account, as long as they can be automatically extracted from example texts.