Towards generating a patient’s timeline: Extracting temporal relationships from clinical notes

• We proposed a hybrid system (graph-based and machine-learning) for temporal relation extraction.
• Our system yields an f-measure of 0.63, precision of 0.76 and recall of 0.54.
• We ranked third in f-measure and first in precision in the 2012 i2b2 NLP TLink extraction task.
• Sentence simplification and then parsing improves temporal relation extraction.
• Generalizing sentences yields interesting extraction patterns.

Clinical records include both coded and free-text fields that interact to reflect complicated patient stories. The information often covers not only the present medical condition and events experienced by the patient, but also refers to relevant events in the past (such as signs, symptoms, tests or treatments). In order to automatically construct a timeline of these events, we first need to extract the temporal relations between pairs of events or time expressions presented in the clinical notes. We designed separate extraction components for different types of temporal relations, utilizing a novel hybrid system that combines machine learning with a graph-based inference mechanism to extract the temporal links. The temporal graph is a directed graph based on parse tree dependencies of the simplified sentences and frequent pattern clues. We generalized the sentences in order to discover patterns that, given the complexities of natural language, might not be directly discoverable in the original sentences. The proposed hybrid system performance reached an F-measure of 0.63, with precision at 0.76 and recall at 0.54 on the 2012 i2b2 Natural Language Processing corpus for the temporal relation (TLink) extraction task, achieving the highest precision and third highest f-measure among participating teams in the TLink track.

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