An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
An Empirical Comparison of Explainable Artificial Intelligence Methods for Clinical Data: A Case Study on Traumatic Brain Injury
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DOI：
10.48550/arxiv.2208.06717
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发表时间：
2022-08
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影响因子：
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通讯作者：
Amin Nayebi;Sindhu Tipirneni;B. Foreman;Chandan K. Reddy;V. Subbian
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作者：
Amin Nayebi;Sindhu Tipirneni;B. Foreman;Chandan K. Reddy;V. Subbian
围绕深度学习算法的一个长期挑战是解开并理解它们如何做出决策。可解释的人工智能 (XAI) 提供了一些方法，以人类用户可解释和理解的方式解释算法的内部功能及其决策背后的原因。 。迄今为止，已经开发了许多 XAI 方法，并且似乎有必要对这些策略进行比较分析，以辨别它们与临床预测模型的相关性。为此，我们首先分别利用结构化表格和时间序列生理数据实现了创伤性脑损伤（TBI）短期和长期结果的两个预测模型。使用六种不同的解释技术来描述局部和全球层面的预测模型。然后，我们对每种策略的优点和缺点进行了批判性分析，强调了对有兴趣应用这些方法的研究人员的影响。对所实施的方法在几个 XAI 特性（例如可理解性、保真度和稳定性）方面进行了相互比较。我们的研究结果表明，SHAP 是最稳定、保真度最高的，但缺乏可理解性。另一方面，锚点是最容易理解的方法，但它仅适用于表格数据，不适用于时间序列数据。
A longstanding challenge surrounding deep learning algorithms is unpacking and understanding how they make their decisions. Explainable Artificial Intelligence (XAI) offers methods to provide explanations of internal functions of algorithms and reasons behind their decisions in ways that are interpretable and understandable to human users. . Numerous XAI approaches have been developed thus far, and a comparative analysis of these strategies seems necessary to discern their relevance to clinical prediction models. To this end, we first implemented two prediction models for short- and long-term outcomes of traumatic brain injury (TBI) utilizing structured tabular as well as time-series physiologic data, respectively. Six different interpretation techniques were used to describe both prediction models at the local and global levels. We then performed a critical analysis of merits and drawbacks of each strategy, highlighting the implications for researchers who are interested in applying these methodologies. The implemented methods were compared to one another in terms of several XAI characteristics such as understandability, fidelity, and stability. Our findings show that SHAP is the most stable with the highest fidelity but falls short of understandability. Anchors, on the other hand, is the most understandable approach, but it is only applicable to tabular data and not time series data.