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

Azadeh Nikfarjam, Ehsan Emadzadeh, Graciela Gonzalez

    Research output: Contribution to journalArticlepeer-review

    31 Scopus citations


    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.

    Original languageEnglish (US)
    Pages (from-to)S40-S47
    JournalJournal of Biomedical Informatics
    Issue numberSUPPL.
    StatePublished - Dec 2013


    • Automatic patient timeline
    • Clinical text mining
    • Machine learning
    • Natural Language Processing
    • Temporal graph
    • Temporal relation extraction

    ASJC Scopus subject areas

    • Computer Science Applications
    • Health Informatics


    Dive into the research topics of 'Towards generating a patient's timeline: Extracting temporal relationships from clinical notes'. Together they form a unique fingerprint.

    Cite this