Relative and Incomplete Time Expression Anchoring for Clinical Text (2020)

Abstract: Extracting and modeling temporal information in clinical text is an important element for developing timelines and disease trajectories. Time information in written text varies in preciseness and explicitness, posing challenges for NLP approaches that aim to accurately anchor temporal information on a timeline. Relative and incomplete time expressions (RI-Timexes) are expressions that require additional information for their temporal anchor to be resolved, but few studies have addressed this challenge specifically. In this study, we aimed to reproduce and verify a classification approach for identifying anchor dates and relations in clinical text, and propose a novel relation classification approach for this task.

## introduction temporal information is a crucial aspect of the analysis of clinical texts in electronic health records in order to improve understanding of disease trajectories. being able to extract and model time information, such as dates and durations of events, leads to knowledge about the temporal context of clinically important information like symptoms or treatments, and can be used e.g. to reconstruct a patient's timeline. with such timelines, a wide range of applications can be developed, such as population-based observational retrospective studies on temporal patterns in diseases and treatments, or individualised patient summaries. several solutions have been proposed and developed to extract and normalize temporal information from text both in the general and clinical nlp domains @xcite @xcite . the most widely used model for annotating temporal information and cues for nlp applications is the timeml model @xcite , where time expressions (timexes) are a core element. these are typically annotated into types (e.g. dates, durations) and normalized to a temporal value that can be used for further temporal reasoning. however, accurate normalization of relative and incomplete temporal expressions is still an understudied area. relative and incomplete time expressions (ri-timexes), as defined in @xcite are time expressions that require another timex for their value to be resolved. for example, in the following sentences "he arrived on 09/18/2002. three days later, he was transferred to the medical intensive care unit.", the normalized temporal value of the date timex "09/18/2002" does not depend on any context. such expressions are called absolute timexes. ri-timexes, on the other hand, require additional contextual information. for example, to assign and compute the normalized temporal value of the ri-timex "three days later", we need information about what this expression refers to in the narrative -in this case, the previous date timex "09/18/2002". the temporal expression that the relative timex refers to is called the anchor. an anchor relation, which specifies the link between the two expressions, can also be defined. with these two pieces of information, it becomes feasible to compute a normalized value for the ri-timex (09/21/2002 in this case). in the clinical domain, two of the most widely used temporal extraction and normalization tools are the java-based libraries heideltime @xcite and sutime @xcite . their approach to normalize relative time expressions is to define one main anchor date for the whole document (document creation time, dct), and all timexes in the document are resolved relatively to this. this method might work well on e.g. short texts that refer to a single event, but is not necessarily appropriate for longer narrative notes, for example clinical assessments relating to a patient's history. adaptations and variations of these systems were used by several teams in the 2012 i2b2 nlp challenge on temporal relations in clinical data @xcite , and the best performing system on timex normalization yielded value accuracy of 0.73 @xcite . in the analysis of these results, it was found that relative time expressions were a major source of submitted system's errors. in the proposed solutions, the main approaches relied on either defining a single anchor date (the dct) for the whole text, or creating a set of rules that anchors expressions based on specific signal words, such as "operation", or "birth" @xcite . according to @xcite , almost all current state-of-the-art nlp systems use handcrafted rules based on lexical patterns to solve timex normalization. however, such rules have limitations, and not many deal with anchoring ri-timexes in clinical notes. one study specifically addresses the problem of anchoring ri-timexes @xcite . they propose two simplification hypotheses, for identifying and classifying the anchor date and anchor relation respectively: they restrict the anchor date possibilities to four different temporal expressions (admission date, discharge date, previous timex, and previous absolute timex), and the anchor relation to three possibilities (before, after, and equal/during). this allows them to approach the problem as a multi-class classification problem. they manually annotated three corpora following these hypotheses, and proposed a supervised machine-learning approach, along with a rule-based approach for the final relative value normalization. to our knowledge, there have been no further studies on alternative approaches for identifying and classifying anchor dates and relations for ri-timexes in clinical text. our long-term goal is to develop approaches for modelling time information that can be used for clinical timeline reconstruction, for which novel approaches for identifying, anchoring and normalizing ri-timexes are needed. our contribution in this study is a) we aimed to reproduce the findings published in @xcite , allowing to verify the viability of their hypothesis, and to define a baseline against which we could compare new approaches; b) we propose an alternative annotation model for anchoring ri-timexes, and developed and applied a new, adapted, annotation model; and c) we propose a new computational approach and model the problem as a relation classification problem, using a bert transformer model @xcite trained on clinical data @xcite . foot_0 2 materials and methods ## results we report results for our annotation process: the inter-annotator agreements, and a comparison between our annotations and the annotations from @xcite . we also report results for our classification experiments: the two attempts to reproduce @xcite 's methodology with distinct datasets and our anchor date predictions with a fine-tuned bert model. ## discussion and conclusion we present a study on identifying and classifying anchor dates and relations specifically for ri-timexes in clinical text. we attempted to reproduce the findings by @xcite , in order to produce a baseline against which we could compare new approaches. because the full dataset used in that study was not available, we developed new guidelines and produced a new reference standard of annotations with anchor date types and relations (2020 ri-timex i2b2 data). we applied is b e a avg valid. p 85.2 85.0 86.5 83.0 85.5 r 88.2 94.4 81.0 86.1 86.7 f 86.7 89.4 83.7 84.5 85.8 test p 34.0 35.4 29.4 29.4 32.2 r 76.2 60.5 57.2 72.1 70.3 f 47.0 44.6 39.2 41.8 44.1