There is a need of ensuring machine learning models that are interpretable.
Higher interpretability of the model means easier comprehension and explanation
of future predictions for end-users. Further, interpretable machine learning
models allow healthcare experts to make reasonable and data-driven decisions to
provide personalized decisions that can ultimately lead to higher quality of
service in healthcare. Generally, we can classify interpretability approaches
in two groups where the first focuses on personalized interpretation (local
interpretability) while the second summarizes prediction models on a population
level (global interpretability). Alternatively, we can group interpretability
methods into model-specific techniques, which are designed to interpret
predictions generated by a specific model, such as a neural network, and
model-agnostic approaches, which provide easy-to-understand explanations of
predictions made by any machine learning model. Here, we give an overview of
interpretability approaches and provide examples of practical interpretability
of machine learning in different areas of healthcare, including prediction of
health-related outcomes, optimizing treatments or improving the efficiency of
screening for specific conditions. Further, we outline future directions for
interpretable machine learning and highlight the importance of developing
algorithmic solutions that can enable machine-learning driven decision making
in high-stakes healthcare problems.

该研究论文概述了理解机器学习模型的需求，解释了分类理解方法并提供了在不同医疗保健领域实际应用机器学习可解释性的例子，强调开发算法解决高风险医疗问题的重要性。