  Electronic health records (EHRs), which contain patients' medical histories,
tend to be written in freely formatted (unstructured) text because they are
complicated by their nature. Quickly understanding a patient's history is
challenging and critical because writing styles vary among doctors, which may
even cause clinical incidents. This paper proposes a Health Record Timeliner
system (HeaRT), which visualises patients' clinical histories directly from
natural language text in EHRs. Unlike only a few previous attempts, our system
achieved feasible and practical performance for the first time, by integrating
a state-of-the-art language model that recognises clinical entities (e.g.
diseases, medicines, and time expressions) and their temporal relations from
the raw text in EHRs and radiology reports. By chronologically aligning the
clinical entities to the clinical events extracted from a medical report, this
web-based system visualises them in a Gantt chart-like format. Our novel
evaluation method showed that the proposed system successfully generated
coherent timelines from the two sets of radiology reports describing the same
CT scan but written by different radiologists. Real-world assessments are
planned to improve the remaining issues.
