REDIT: A Tool and Dataset for Extraction of Personal Data in Documents of the Public Administration Domain
Authors: Teresa Paccosi, Alessio Palmero Aprosio (Fondazione Bruno Kessler, 2021)
Topic: In-domain IR and IE

Abstract
This paper aims at uncovering the structure of clinical documents, in particular, identifying paragraphs describing “diagnosis” or “procedures”. We present transformer-based architectures for approaching this task in a monolingual setting (English), exploring a weak supervision scheme. We further extend our contribution to a cross-lingual scenario, mitigating the need for expensive manual data annotation and taxonomy engineering for Italian.

Introduction
Big Data approaches have been shown to yield a breakthrough to a variety of healthcare-related tasks, ranging from eHealth governance and policy making  to precision medicine and smart solutions/suites for hospitals or individual doctors.  They rely on large-scale and reliable automatic processing of vast amounts of heterogeneous data, i.e., images, lab reports and, most importantly, textual medical documentation.  The current paper focuses on Medical Discourse Analysis: imposing structure on digitalized health reports through document segmentation and labeling of relevant segments (e.g., ). Identifying and interpreting discourse fragments is essential for accurate and robust Information Extraction from medical documents. In terms of doctor assistance, such a system could quickly and reliably identify  the most crucial parts of voluminous health records, allowing to highlight them for improved visibility and thus  reducing cognitive load on doctors. For example, a highlighted problematic diagnosis can alert a doctor perusing a large medical dossier.  In terms of automated data analytics, discourse structure is crucial for correct interpretation of extracted information. For example, if we want to study a possible correlation between the use of a specific medicine and some outcome, we should only consider documents where this medicine is mentioned as a part of , but not as a part of .  Some medical documents are generated using task-specific eHealth software  imposing certain discourse structure. In Italy, however, there is no single software adopted at either national or regional levels. While there is a general agreement on the nature of information to be included,  there are no guidelines or programmatic implementations for structuring it. In addition, historical records, produced before the adoption of recording software, follow the logic of individual doctors and thus show even more variability.  We aim therefore  at a statistical model that is able to infer the discourse structure without making any assumptions on the recording software.   An important advantage of our approach is its adaptability to new domains (e.g., radiology reports) or languages as well as its robustness in the (highly probable) scenario where new report-generating systems appear at the market.   Several recent studies (Section <ref>) focus on segment labeling for medical records in English. To our knowledge, no approach has been proposed so far to analyze medical discourse structure automatically in other languages, including, most importantly, Italian. The required research is hampered by the lack of resources in other languages, ranging from no data annotated for discourse structure, either for training or for benchmarking, to lack of high-coverage resources, e.g., taxonomies, required for such projects. In our study, we propose a language transfer approach to the problem of medical discourse analysis in Italian. We first investigate possibilities for training robust monolingual models (Section <ref>) and then build upon our monolingual results to transfer the model in another language (Section <ref>).

Conclusion
This paper presents a language transfer approach to unraveling discourse structure of clinical notes, focusing on diagnosis and procedure. We combine transformer-based paragraph modeling with state-of-the-art MT architectures in a novel application, that is essential for eHealth big data analytics. Most importantly, our language transfer approach helps mitigate the need for expensive and time-consuming medical resource creation (annotated train data as well as header taxonomy) in Italian. We empirically investigate two translation-based architectures, showing that both of them outperform a generic cross-lingual pipeline. The approach based on translating train data is more robust and efficient (at runtime) compared to translating the test data, yielding more stable performance. In future, we plan to expand our study to cover other types of discourse segments, such as allergy or history. However, our first experiments with procedure segments show that, unlike diagnosis, modeling and even annotating other headers require a more tight collaboration with medical experts.