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Development and validation of an artificial intelligence model for the early classification of the aetiology of meningitis and encephalitis: a retrospective observational study

Authors
 Bo Kyu Choi  ;  Young Jo Choi  ;  MinDong Sung  ;  WooSeok Ha  ;  Min Kyung Chu  ;  Won-Joo Kim  ;  Kyoung Heo  ;  Kyung Min Kim  ;  Yu Rang Park 
Citation
 ECLINICALMEDICINE, Vol.61 : 102051, 2023-07 
Journal Title
ECLINICALMEDICINE
Issue Date
2023-07
Keywords
Artificial intelligence ; Autoimmune encephalitis ; Meningitis ; Neuroinflammation ; Tuberculosis
Abstract
Background: Early diagnosis and appropriate treatment are essential in meningitis and encephalitis management. We aimed to implement and verify an artificial intelligence (AI) model for early aetiological determination of patients with encephalitis and meningitis, and identify important variables in the classification process.

Methods: In this retrospective observational study, patients older than 18 years old with meningitis or encephalitis at two centres in South Korea were enrolled for development (n = 283) and external validation (n = 220) of AI models, respectively. Their clinical variables within 24 h after admission were used for the multi-classification of four aetiologies including autoimmunity, bacteria, virus, and tuberculosis. The aetiology was determined based on the laboratory test results of cerebrospinal fluid conducted during hospitalization. Model performance was assessed using classification metrics, including the area under the receiver operating characteristic curve (AUROC), recall, precision, accuracy, and F1 score. Comparisons were performed between the AI model and three clinicians with varying neurology experience. Several techniques (eg, Shapley values, F score, permutation feature importance, and local interpretable model-agnostic explanations weights) were used for the explainability of the AI model.

Findings: Between January 1, 2006, and June 30, 2021, 283 patients were enrolled in the training/test dataset. An ensemble model with extreme gradient boosting and TabNet showed the best performance among the eight AI models with various settings in the external validation dataset (n = 220); accuracy, 0.8909; precision, 0.8987; recall, 0.8909; F1 score, 0.8948; AUROC, 0.9163. The AI model outperformed all clinicians who achieved a maximum F1 score of 0.7582, by demonstrating a performance of F1 score greater than 0.9264.

Interpretation: This is the first multiclass classification study for the early determination of the aetiology of meningitis and encephalitis based on the initial 24-h data using an AI model, which showed high performance metrics. Future studies can improve upon this model by securing and inputting time-series variables and setting various features about patients, and including a survival analysis for prognosis prediction.

Funding: MD-PhD/Medical Scientist Training Program through the Korea Health Industry Development Institute, funded by the Ministry of Health & Welfare, Republic of Korea.
Files in This Item:
T202304171.pdf Download
DOI
10.1016/j.eclinm.2023.102051
Appears in Collections:
1. College of Medicine (의과대학) > Dept. of Biomedical Systems Informatics (의생명시스템정보학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Internal Medicine (내과학교실) > 1. Journal Papers
1. College of Medicine (의과대학) > Dept. of Neurology (신경과학교실) > 1. Journal Papers
Yonsei Authors
Kim, Kyung Min(김경민) ORCID logo https://orcid.org/0000-0002-0261-1687
Kim, Won Joo(김원주) ORCID logo https://orcid.org/0000-0002-5850-010X
Park, Yu Rang(박유랑) ORCID logo https://orcid.org/0000-0002-4210-2094
Sung, MinDong(성민동) ORCID logo https://orcid.org/0000-0002-5217-8877
Chu, Min Kyung(주민경) ORCID logo https://orcid.org/0000-0001-6221-1346
Choi, Bo Kyu(최보규) ORCID logo https://orcid.org/0000-0002-0796-4043
Ha, Woo Seok(하우석) ORCID logo https://orcid.org/0000-0003-1188-449X
Heo, Kyoung(허경)
URI
https://ir.ymlib.yonsei.ac.kr/handle/22282913/195972
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