1. Machine-Learning-Based Approach to Differential Diagnosis in Tuberculous and Viral Meningitis
- Author
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Min Huok Jeon, Young-Seob Jeong, Ki-Ho Park, Eun Ju Choo, Seong Yeon Park, Sungim Choi, Mi Suk Lee, Min-Chul Kim, Joung Ha Park, Tark Kim, Eun Young Lee, Se Yoon Park, Sung-Han Kim, Tae Hyong Kim, Minjun Jeon, and Yu-Mi Lee
- Subjects
medicine.medical_specialty ,urologic and male genital diseases ,Tuberculous meningitis ,03 medical and health sciences ,0302 clinical medicine ,Internal medicine ,Machine learning ,Diagnosis ,medicine ,Viral meningitis ,Tuberculosis ,Meningitis ,Pharmacology (medical) ,030212 general & internal medicine ,CSF albumin ,0303 health sciences ,Receiver operating characteristic ,030306 microbiology ,business.industry ,Area under the curve ,medicine.disease ,Confidence interval ,Virus ,Infectious Diseases ,Original Article ,Differential diagnosis ,business - 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 The training study comprised data from 60 patients with confirmed or probable TBM and 143 patients with confirmed VM. Older age, longer symptom duration before the visit, lower serum sodium, lower cerebrospinal fluid (CSF) glucose, higher CSF protein, and CSF adenosine deaminase were found in the TBM patients. Among the various machine-learning algorithms, the area under the curve (AUC) of the receiver operating characteristics of artificial neural network (ANN) with ImperativeImputer for matrix completion (0.85; 95% confidence interval 0.79 - 0.89) was found to be the highest. The AUC of the ANN model was statistically higher than those of all the residents (range 0.67 - 0.72, P
- Published
- 2021
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