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Background <p>Accurate assessment of consciousness in patients with disorders of consciousness (DoC) remains a major clinical challenge in both specialized neurorehabilitation and acute care settings, where behavioral evaluations alone can lead to misdiagnosis rates of up to 40%. This study aimed to develop and validate an explainable multimodal machine learning (ML) model to improve diagnostic precision in DoC.</p> Methods <p>Leveraging one of the largest multicenter cohorts of patients with DoC in China to date (<i>n</i> = 583), together with an independent external validation set (<i>n</i> = 70), we examined whether routinely collected clinical indicators together with EEG features could improve diagnostic reliability. Using LASSO-based feature selection, we developed three predictive ML models (clinical, EEG, and a combined clinical-EEG model). Nine ML algorithms were tested, with hyperparameters optimized by grid search and cross-validation. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis (DCA). Model interpretability was examined using SHapley Additive exPlanations (SHAP).</p> Results <p>The combined clinical-EEG model (Model 3) consistently outperformed single-modality models. The final logistic regression-based Model 3 achieved an AUC of 0.77 (95% CI: 0.68–0.85) in the training set, 0.76 (95% CI: 0.61–0.92) in the internal validation set, and 0.75 (95% CI: 0.64–0.87) in the external validation set. SHAP analysis identified eight key features that contributed most to classification, providing biologically and clinically interpretable insights. To support clinical translation and time-sensitive decision-making, we developed a web-based tool enabling automated EEG feature extraction and individualized diagnostic predictions.</p> Conclusions <p>This study developed and validated an explainable ML model integrating clinical and EEG features to classify consciousness levels in DoC, based on the large-multicenter DoC cohorts in China. Taken together, these findings suggest that a transparent multimodal framework can strengthen the clinical evaluation of consciousness and may offer a practical template for improving assessment in complex care environments.</p> Trail registration <p>ClinicalTrials.gov: NCT05949528. Registered 14 March 2022, <a href="https://clinicaltrials.gov/study/NCT05949528">https://clinicaltrials.gov/study/NCT05949528</a>.</p>

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An explainable multimodal machine learning model for diagnosing disorders of consciousness: evidence from a large multicenter Chinese cohort

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年4月15日

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  • Chenye Mou,
  • Jiajia Zhao,
  • Zilong Yan,
  • Li Zhang,
  • Xuejiao Tian,
  • Yingchen Wang,
  • Hanxiao Wang,
  • Jun Hu,
  • Zhuolin He,
  • Yi Ling,
  • Anshun Kang,
  • Qiwen Luo,
  • Jian Gao,
  • Xiangming Ye,
  • Lirong Hong,
  • Jingqi Li,
  • Tianyi Yan,
  • Jie Yu,
  • Benyan Luo

摘要

Background

Accurate assessment of consciousness in patients with disorders of consciousness (DoC) remains a major clinical challenge in both specialized neurorehabilitation and acute care settings, where behavioral evaluations alone can lead to misdiagnosis rates of up to 40%. This study aimed to develop and validate an explainable multimodal machine learning (ML) model to improve diagnostic precision in DoC.

Methods

Leveraging one of the largest multicenter cohorts of patients with DoC in China to date (n = 583), together with an independent external validation set (n = 70), we examined whether routinely collected clinical indicators together with EEG features could improve diagnostic reliability. Using LASSO-based feature selection, we developed three predictive ML models (clinical, EEG, and a combined clinical-EEG model). Nine ML algorithms were tested, with hyperparameters optimized by grid search and cross-validation. Model performance was assessed using ROC curves, calibration plots, and decision curve analysis (DCA). Model interpretability was examined using SHapley Additive exPlanations (SHAP).

Results

The combined clinical-EEG model (Model 3) consistently outperformed single-modality models. The final logistic regression-based Model 3 achieved an AUC of 0.77 (95% CI: 0.68–0.85) in the training set, 0.76 (95% CI: 0.61–0.92) in the internal validation set, and 0.75 (95% CI: 0.64–0.87) in the external validation set. SHAP analysis identified eight key features that contributed most to classification, providing biologically and clinically interpretable insights. To support clinical translation and time-sensitive decision-making, we developed a web-based tool enabling automated EEG feature extraction and individualized diagnostic predictions.

Conclusions

This study developed and validated an explainable ML model integrating clinical and EEG features to classify consciousness levels in DoC, based on the large-multicenter DoC cohorts in China. Taken together, these findings suggest that a transparent multimodal framework can strengthen the clinical evaluation of consciousness and may offer a practical template for improving assessment in complex care environments.

Trail registration

ClinicalTrials.gov: NCT05949528. Registered 14 March 2022, https://clinicaltrials.gov/study/NCT05949528.