Data-driven healthcare uses predictive analytics to enhance decision-making and personalized healthcare. Developing prognostic models is one of the applications of predictive analytics in medical environments. Various studies have used machine learning techniques for this purpose. However, there is no specific standard for choosing prediction models for different medical purposes. In this paper, the ISAF framework was proposed for choosing appropriate prediction models with regard to the properties of the classification methods. As one of the case study applications, a prognostic model for predicting cardiac arrests in sepsis patients was developed step by step through the ISAF framework. Finally, a new modified stacking model produced the best results. We predict 85 % of heart arrest cases one hour before the incidence (sensitivity> = 0.85) and 73 % of arrest cases 25 h before the occurrence (sensitivity> = 0.73). The results indicated that the proposed prognostic model has significantly improved the prediction results compared to the two standard systems of APACHE II and MEWS. Furthermore, compared to previous research, the proposed model has extended the prediction interval and improved the performance criteria.

数据驱动的医疗保健使用预测分析来增强决策和个性化医疗保健。开发预后模型是预测分析在医疗环境中的应用之一。为此，各种研究都使用了机器学习技术。但是，针对不同的医学目的选择预测模型并没有具体的标准。在本文中，ISAF 框架被提出用于根据分类方法的特性选择合适的预测模型。作为案例研究应用之一，通过 ISAF 框架逐步开发了一种用于预测脓毒症患者心脏骤停的预后模型。最后，一个新的修改堆叠模型产生了最好的结果。我们预测 85% 的心脏骤停病例发生在发病前一小时（敏感性> = 0。85) 和 73 % 的逮捕案例发生前 25 小时（敏感性> = 0.73）。结果表明，与APACHE II和MEWS两个标准系统相比，所提出的预后模型显着提高了预测结果。此外，与之前的研究相比，所提出的模型扩展了预测区间并提高了性能标准。