  In response to the COVID-19 pandemic, the integration of interpretable
machine learning techniques has garnered significant attention, offering
transparent and understandable insights crucial for informed clinical decision
making. This literature review delves into the applications of interpretable
machine learning in predicting the prognosis of respiratory diseases,
particularly focusing on COVID-19 and its implications for future research and
clinical practice. We reviewed various machine learning models that are not
only capable of incorporating existing clinical domain knowledge but also have
the learning capability to explore new information from the data. These models
and experiences not only aid in managing the current crisis but also hold
promise for addressing future disease outbreaks. By harnessing interpretable
machine learning, healthcare systems can enhance their preparedness and
response capabilities, thereby improving patient outcomes and mitigating the
impact of respiratory diseases in the years to come.
