Automatic Generation of Electromyogram Diagnosis Report

Qizheng Gu, Cong Nie, Ruixiang Zou, Wei Chen, Chaojun Zheng, Dongqing Zhu, Xiaojun Mao, Zhongyu Wei, Dong Tian

Abstract: Electrophysiological tests, especially, electromyogram (EMG) and nerve conduction velocity (NCV) test are commonly used in clinical practice for diagnosis of muscle and nerve diseases. Report-writing of these tests can be problematic for under-experienced physicians and time-consuming for experienced physicians. In this paper, we apply several neural based natural language generation (NLG) methods to automatically generate diagnosis reports, a first attempt in this domain. Specifically, we use tabular diagnostic records of electrophysiological tests to generate Findings & Impression, which together constitute the diagnostic report. We further use gram-based metrics to evaluate our models and conduct a case study for the result.