Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task

@article{Wu2021InterpretableML,
  title={Interpretable Machine Learning for COVID-19: An Empirical Study on Severity Prediction Task},
  author={Han Wu and Wenjie Ruan and Jiangtao Wang and Dingchang Zheng and Shaolin Li and Jian Chen and Kunwei Li and Xiangfei Chai and Abdelsalam Helal},
  journal={ArXiv},
  year={2021},
  volume={abs/2010.02006}
}
Black-box nature hinders the deployment of many high-accuracy models in medical diagnosis. It is risky to put one's life in the hands of models that medical researchers do not trust. However, to understand the mechanism of a new virus, such as COVID-19, machine learning models may catch important symptoms that medical practitioners do not notice due to the surge of infected patients during a pandemic. In this work, the interpretation of machine learning models reveals that a high C-reactive… 
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