Event-independent temporal positioning: application to F rench clinical text (2023)

Abstract: Extracting temporal relations usually entails identifying and classifying the relation between two mentions. However, the definition of temporal mentions strongly depends on the text type and the application domain. Clinical text in particular is complex. It may describe events that occurred at different times, contain redundant information and a variety of domain-specific temporal expressions. In this paper, we propose a novel event-independent representation of temporal relations that is task-independent and, therefore, domain-independent. We are interested in identifying homogeneous text portions from a temporal standpoint and classifying the relation between each text portion and the document creation time. Temporal relation extraction is cast as a sequence labeling task and evaluated on oncology notes. We further evaluate our temporal representation by the temporal positioning of toxicity events of chemotherapy administrated to colon and lung cancer patients described in French clinical reports. An overall macro F-measure of 0.86 is obtained for temporal relation extraction by a neural token classification model trained on clinical texts written in French. Our results suggest that the toxicity event extraction task can be performed successfully by automatically identifying toxicity events and placing them within the patient timeline (F-measure .62). The proposed system has the potential to assist clinicians in the preparation of tumor board meetings.

## introduction temporal information extraction from text is a critical task in natural language processing (nlp) research, and it has been employed in a wide range of nlp applications, including narrative construction @xcite @xcite , temporal question answering @xcite , and clinical text processing @xcite @xcite . temporal information extraction was first addressed in the news article domain @xcite @xcite . at the same time, there has been a significant interest in temporal information extraction from clinical narratives through the i2b2-2012 challenge @xcite and the clinical tempeval shared tasks @xcite @xcite . narrative texts embedded in electronic health records (ehr) contain essential temporal information, which can help better understand the clinical healthcare pathway. temporal information extraction involves detecting events (event), identifying temporal expressions (timex), and extracting temporal relations between them. several challenges arise when representing clinical temporal information @xcite . temporal expressions vary widely, including domainspecific, non-standard, and abbreviated date expressions. moreover, clinical narrative text is often ungrammatical and goes back and forth through time, making it difficult to link the events to temporal expressions. sometimes, the time related to the clinical event is not explicitly specified. redundant information in clinical text is another major problem when determining the chronology of events. temporal relation extraction denotes temporal ordering between text mentions, indicating events or temporal expressions. the timeml @xcite annotation scheme was initially developed to model general-domain events, temporal expressions, and their temporal links (tlinks). thyme-timeml @xcite , a similar annotation scheme adapted to the clinical domain, has been proposed, introducing another form of temporal relations linking clinical events to the document creation time (dct), namely doctimerel. temporal relation extraction models evolved from rule-based models @xcite @xcite to machine learningbased @xcite @xcite and deep learningbased models @xcite @xcite @xcite . however, the performance of the proposed models is still insufficient for practical applications @xcite . indeed, event modeling is task-specific and heavily dependent on the nature of the text, and annotating temporal relations in clinical texts is more challenging since it requires medical expertise, which is costly and time-consuming. as a result, inter-annotator agreement (iaa) is poor in available datasets @xcite , and annotated corpora are limited for non-english languages. only a few studies, in particular, use french corpora @xcite . in this work, we introduce a novel eventindependent representation of temporal relations in clinical texts that is task-independent and easily adaptable to different domains. each narrative portion is assigned to a temporal category, describing its relation to the dct. unlike the doctimerel extraction task, we do not begin by identifying the clinical events and then extracting their relation to the dct. instead, we extract the temporal positioning of each text portion according to the dct, regardless of events. the clinical events could then be identified depending on the task, and each event will have the same temporal positioning as the text portion that includes it. our main contributions are the following: ## related work prior works on temporal relation extraction in clinical text are based on hand-crafted rules. to define these rules, @xcite @xcite used lexical and grammatical features such as part-of-speech (pos) tags, tense and aspect of events. @xcite proposed a model to extract relations between events and time expressions that relies only on the properties of relation entities without the need for extra grammatical information to ensure a minimal dependency on the text quality. rule-based models need human expertise to create domain-specific rules, and such models are difficult to adapt to other domains. later, a variety of supervised machine learning approaches has been used, such as support vector machine (svm) classifiers and conditional random fields (crfs), with different sets of features, including syntactic, lexical, and semantic features @xcite @xcite @xcite . @xcite presented a model based on the random forest (rf) algorithm, and @xcite used a logistic regression (lr) classifier to extract the temporal relations between clinical events and the dct. @xcite addresses the doctimerel task by using a crf model and the narrative container relation sub-task by building a hybrid approach based on crf and a rule-based technique. @xcite also proposed a hybrid model combining a rule-based method with a maximum entropy model. deep neural networks have been used for temporal relation extraction in recent years. @xcite utilized convolution neural networks (cnns) to identify the relation between events and dct. @xcite proposed models based on cnns and long short-term memory (lstm) for extracting the event-event and event-time contains relations from the thyme corpus. @xcite used lstms to create inter-sentence and intra-sentence relation classifiers. @xcite obtained state-ofart performance on the 2016 clinical tempeval challenge for temporal relation extraction using a tree-based lstm model relying on dependency information. @xcite proposed an attention mechanism to enhance the overall performance of lstm and gru neural models for containment relations. @xcite investigated the attention mechanism built into a bi-lstm model on a broader set of temporal relations in clinical discharge summaries, including intra-sentence, crosssentence, and doctimerel temporal relations. @xcite introduced bert (kenton and toutanova, 2019) based models using the combination of global embeddings and multi-task learning to extract tlinks and doctimerel relations jointly. supervised machine learning and deep learning models require large amounts of annotated data. however, few annotated corpora in the clinical domain are available in languages other than english. as a result, few research efforts addressed french corpora @xcite . ## results figure 2 shows a clinical text sample with predicted results of temporal positioning of homogeneous text portions. ## conclusion in this paper, we introduced a novel eventindependent representation of temporal relations that is task-independent and, hence, domainindependent. the temporal relation classification problem is cast as a sequence token classification task using our representation. the main goal of this classification task is to identify homogeneous text portions and to classify them into temporal categories reflecting their relations with the document creation time. to develop and evaluate our model, we annotate a corpus of clinical reports written in french using the thyme-timeml annotation scheme. our temporal positioning model yields good results when recognizing and categorizing text portions. moreover, experiments on the temporal positioning of chemotherapy toxicity events for patients with colon and lung cancers have also shown that good results could be achieved using our representation of temporal relations. this problem modeling might be the initial step toward constructing a patient timeline to order all its medical events.