IEEE Rev Biomed Eng. 2021;14:116-126. doi: 10.1109/RBME.2020.3007816. Epub 2021 Jan 22.
Clinical decision-making in healthcare is already being influenced by predictions or recommendations made by data-driven machines. Numerous machine learning applications have appeared in the latest clinical literature, especially for outcome prediction models, with outcomes ranging from mortality and cardiac arrest to acute kidney injury and arrhythmia. In this review article, we summarize the state-of-the-art in related works covering data processing, inference, and model evaluation, in the context of outcome prediction models developed using data extracted from electronic health records. We also discuss limitations of prominent modeling assumptions and highlight opportunities for future research.
在医疗保健领域，临床决策已经受到数据驱动的机器做出的预测或建议的影响。最近的临床文献中出现了许多机器学习应用，特别是在预后预测模型方面，涵盖了从死亡率和心搏骤停到急性肾损伤和心律失常等各种结果。在这篇综述文章中，我们总结了在使用从电子健康记录中提取的数据开发预后预测模型的背景下，涉及数据处理、推断和模型评估的相关工作的最新技术状态。我们还讨论了突出建模假设的局限性，并强调了未来研究的机会。
