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논문 기본 정보

자료유형
학술저널
저자정보
Jeong Young-Seob (Big Data Engineering department Soonchunhyang University Asan Korea.) Jeon Minjun (R&D department PharmCADD Busan Korea.) Park Joung Ha (Department of Infectious Diseases Asan Medical Center University of Ulsan College of Medicine Seoul) Kim Min-Chul (Department of Infectious Diseases Asan Medical Center University of Ulsan College of Medicine Seoul) Lee Eunyoung (Department of Internal Medicine Soonchunhyang University Seoul Hospital Seoul Korea.Division of Inf) Park Se Yoon (Department of Internal Medicine Soonchunhyang University Seoul Hospital Seoul Korea.) Lee Yu-Mi (Department of Internal Medicine Kyung Hee University Hospital Kyung Hee University School of Medici) Choi Sungim (Division of Infectious Diseases Dongguk University Ilsan Hospital Goyang Korea.) Park Seong Yeon (Division of Infectious Diseases Dongguk University Ilsan Hospital Goyang Korea.) Park Ki-Ho (Department of Internal Medicine Kyung Hee University Hospital Kyung Hee University School of Medici) Kim Sung-Han (Department of Infectious Diseases Asan Medical Center University of Ulsan College of Medicine Seoul) Jeon Min Huok (Department of Internal Medicine Soonchunhyang University Cheonan Hospital Cheonan Korea.) Choo Eun Ju (Department of Internal Medicine Soonchunhyang University Bucheon Hospital Bucheon Korea.) Kim Tae Hyong (Department of Internal Medicine Soonchunhyang University Seoul Hospital Seoul Korea.) Lee Mi Suk (Department of Internal Medicine Kyung Hee University Hospital Kyung Hee University School of Medici) Kim Tark (Department of Internal Medicine Soonchunhyang University Bucheon Hospital Bucheon Korea.)
저널정보
대한감염학회 Infection and Chemotherapy Infection and Chemotherapy 제53권 제1호
발행연도
2021.1
수록면
53 - 62 (10page)

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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 <0.001) and an infectious disease specialist (AUC 0.76; P = 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.
#Tuberculosis #Virus #Meningitis #Machine learning #Diagnosis

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참고문헌 (14)

참고문헌 신청
World Health Organization (WHO) / 2018 / Global tuberculosis report 2018 google schola Wilkinson RJ / 2017 / Tuberculous meningitis / Nat Rev Neurol 13:581~598 google schola Erdem H / 2014 / The microbiological diagnosis of tuberculous meningitis: results of Haydarpasa-1 study / Clin Microbiol Infect 20:O600~O608 google schola Solomons RS / 2014 / Commercial nucleic acid amplification tests in tuberculous meningitis--a meta-analysis / Diagn Microbiol Infect Dis 78:398~403 google schola Nhu NT / 2014 / Evaluation of GeneXpert MTB/RIF for diagnosis of tuberculous meningitis / J Clin Microbiol 52:226~233 google schola

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