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Electronic health records (EHRs), which contain patients' medical histories,tend to be written in freely formatted (unstructured) text because they arecomplicated by their nature. Quickly understanding a patient's history ischallenging and critical because writing styles vary among doctors, which mayeven cause clinical incidents. This paper proposes a Health Record Timelinersystem (HeaRT), which visualises patients' clinical histories directly fromnatural language text in EHRs. Unlike only a few previous attempts, our systemachieved feasible and practical performance for the first time, by integratinga state-of-the-art language model that recognises clinical entities (e.g.diseases, medicines, and time expressions) and their temporal relations fromthe raw text in EHRs and radiology reports. By chronologically aligning theclinical entities to the clinical events extracted from a medical report, thisweb-based system visualises them in a Gantt chart-like format. Our novelevaluation method showed that the proposed system successfully generatedcoherent timelines from the two sets of radiology reports describing the sameCT scan but written by different radiologists. Real-world assessments areplanned to improve the remaining issues.

HeaRT: Health Record Timeliner to visualise patients' medical history from health record text