Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis
Young-Seob Jeong, Minjun Jeon, Joung Ha Park, Min‐Chul Kim, Eunyoung Lee, Se Yoon Park, Yu‐Mi Lee, Sungim Choi, Seong Yeon Park, Ki–Ho Park, Sung‐Han Kim, Min Hyok Jeon, Eun Ju Choo, Tae Hyong Kim, Mi Suk Lee, Tark Kim
Abstract
BACKGROUND: Tuberculous meningitis (TBM) is the most severe form of tuberculosis, but differentiating between the diagnosis of TBM and viral meningitis (VM) is difficult. Thus, we have developed machine-learning modules for differentiating TBM from VM. MATERIAL AND METHODS: For the training data, confirmed or probable TBM and confirmed VM cases were retrospectively collected from five teaching hospitals in Korea between January 2000 - July 2018. Various machine-learning algorithms were used for training. The machine-learning algorithms were tested by the leave-one-out cross-validation. Four residents and two infectious disease specialists were tested using the summarized medical information. RESULTS: = 0.03). CONCLUSION: The machine-learning techniques may play a role in differentiating between TBM and VM. Specifically, the ANN model seems to have better diagnostic performance than the non-expert clinician.