The E3C Project:Collection and Annotation of a Multilingual Corpus of Clinical Cases
Authors: Bernardo Magnini, Begoña Altuna, Alberto Lavelli, Manuela Speranza, Roberto Zanoli (Fondazione Bruno Kessler, 2020)
Topic: In-domain IR and IE

Abstract
We present the European Clinical Case Corpus (E3C) project, aimed at collecting and annotating a large corpus of clinical cases in five European languages (Italian, English, French, Spanish, and Basque). Project results include: (i) a freely available collection of multilingual clinical cases; and (ii) a two-level annotation scheme based on temporal relations (derived from THYME), whose purpose is to allow the construction of clinical timelines, and taxonomy relations based on medical taxonomies, to be used for semantic reasoning over clinical cases.

Introduction
Identifying clinically relevant events and anchoring them to a chronology is very important in clinical information processing, as the ability to access an ordered sequence of events can help to understand the evolution of clinical conditions in patients. However, although interest in information extraction from clinical narratives has increased in recent decades, attention has been focused on clinical entity extraction and classification (Schulz et al., 2020; Grabar et al., 2019; Dreisbach et al., 2019; Luo et al., 2017) rather than on temporal information. If temporal information is extracted from clinical free text, it can be added to structured data collections, e.g. MIMIC III (Johnson et al., 2016), to train clinical prediction systems. Despite some effort on the organization of clinical narratives processing challenges, e.g. CLEF eHealth (Kelly et al., 2019), few shared training and test data sets have been created, and thus developing tools for this task is still difficult. In fact, the amount of freely available annotated corpora for any of the clinical information extraction tasks has not grown at the same rate as interest in the field, mainly due to patient privacy and data protection issues. In addition, most datasets consist of English texts, which makes research focus on that language. In an attempt to overcome these problems, we present the European Clinical Case Corpus (E3C), a project aimed at offering a freely available multilingual corpus of semantically annotated clinical narratives. The project will build a 5language (Italian, English, French, Spanish, and Basque) clinical narrative corpus to allow for the linguistic analysis, benchmarking, and training of information extraction systems. We build upon available resources and collect new data when necessary, with the goal to harmonize current annotations, introduce new annotation layers, and provide baselines for information extraction tasks. We foresee two types of annotations: (i) temporal information and factuality: events (including attributes expressing factuality-related information), time expressions, and temporal relations according to the THYME standard; and (ii) clinical entities: pathologies, symptoms, procedures, body parts, etc., according to standard clinical taxonomies (e.g. SNOMED-CT (Donnelly, 2006) and ICD-10 (WHO, 2015)). The E3C corpus is organized into three layers, with different purposes: Layer 1: about 25K tokens per language of clinical narratives with full manual or manually checked annotation of clinical entities, temporal information and factuality, for benchmarking and linguistic analysis. Layer 2: 50-100K tokens per language of clinical narratives with automatic annotation of clinical entities and manual check of a small sample (about 10%) of this annotation. Layer 3: about 1M tokens per language of non-annotated medical documents (not necessarily clinical narratives) to be exploited by semisupervised approaches. In this paper we present our data collection effort, focused on clinical cases (Section 3), and we describe our annotation scheme (Section 4).

Conclusion
We presented the E3C project, which aims to become a reference European corpus of annotated clinical cases. We focused on two initial achievements: (i) a freely available collection of clinical cases in five languages; and (ii) a comprehensive annotation scheme based both on temporal information and on medical taxonomies. Our next steps include the extensive manual annotation of the clinical cases in all five languages, and the definition of tasks and baselines on top of the annotated data, taking advantage of neural models derived from training data. More specifically, we plan to target the automatic construction of clinical timelines and question answering over clinical cases.