[Background] To alleviate the huge burden on the medical staff of inputting electronic health records per patient visit, automatic speech recognition has drawn much attention. Although its recent implementation has known difficulties in accurate recognition and medicaldomain adaptation, phrases of the core clinical concepts would still be safely recognized because of their repetitive mentions in patient communications. [Objective] We propose an Entity-to-Text approach that automatically generates the "chief complaints" section of nursing records from a list of clinical named entities. [Material & Methods] Crowdsourced 589 sentences of pseudo "chief complaints" were annotated with patient-status entities. From these entities, a text generation model was trained to replicate the original "chief complaints". [Results & Discussion] The evaluation results showed the basic feasibility of the proposed approach. It also suggested that complete sentences could be generated even from incomplete entities.
