Objective

To build a machine-learning model that predicts laboratory test results and provides a promising lab test reduction strategy, using spatial-temporal correlations.

Materials and methods

We developed a global prediction model to treat laboratory testing as a series of decisions by considering contextual information over time and across modalities. We validated our method using a critical care database (MIMIC III), which includes 4,570,709 observations of 12 standard laboratory tests, among 38,773 critical care patients. Our deep-learning model made real-time laboratory reduction recommendations and predicted the properties of lab tests, including values, normal/abnormal (whether labs were within the normal range) and transition (normal to abnormal or abnormal to normal from the latest lab test). We reported area under the receiver operating characteristic curve (AUC) for predicting normal/abnormal, evaluated accuracy and absolute bias on prediction vs. observation against lab test reduction proportion. We compared our model against baseline models and analyzed the impact of variations on the recommended reduction strategy.

Results

Our best model offered a 20.26 % reduction in the number of laboratory tests. By applying the recommended reduction policy on the hold-out dataset (7755 patients), our model predicted normality/abnormality of laboratory tests with a 98.27 % accuracy (AUC, 0.9885; sensitivity, 97.84 %; specificity, 98.80 %; PPV, 99.01 %; NPV, 97.39 %) on 20.26 % reduced lab tests, and recommended 98.10 % of transitions to be checked. Our model performed better than the greedy models, and the recommended reduction strategy was robust.

Discussion

Strong spatial and temporal correlations between laboratory tests can be used to optimize policies for reducing laboratory tests throughout the hospital course. Our method allows for iterative predictions and provides a superior solution for the dynamic decision-making laboratory reduction problem.

Conclusion

This work demonstrates a machine-learning model that assists physicians in determining which laboratory tests may be omitted.

中文翻译：

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推荐ICU中实验室减少策略的深度学习解决方案

目的

使用时空相关性建立一个预测实验室测试结果并提供有希望的实验室测试减少策略的机器学习模型。

材料和方法

我们开发了一个全局预测模型，通过考虑随时间推移和跨模式出现的上下文信息，将实验室测试视为一系列决策。我们使用重症监护数据库（MIMIC III）验证了我们的方法，该数据库包括38,773名重症监护患者中对12项标准实验室测试的4,570,709观察结果。我们的深度学习模型提出了实时的实验室简化建议，并预测了实验室测试的属性，包括值，正常/异常（实验室是否在正常范围内）和过渡（从最新实验室测试正常到异常或异常到正常） ）。我们报告了接收器工作特性曲线（AUC）下的区域，用于预测正常/异常，评估的准确性和相对于实验室测试减少比例的预测与观察的绝对偏差。

结果

我们最好的模型使实验室测试的数量减少了20.26％。通过对保留的数据集（7755例患者）应用推荐的减少策略，我们的模型预测了实验室测试的正常/异常，准确性为98.27％（AUC为0.9885；敏感性为97.84％；特异性为98.80％； PPV为99.01％ ； NPV，97.39％）减少了20.26％的实验室测试，并建议检查98.10％的过渡。我们的模型比贪婪的模型表现更好，并且推荐的归约策略是可靠的。

讨论区

实验室测试之间的强时空相关性可用于优化策略，以减少整个医院疗程中的实验室测试。我们的方法允许进行迭代预测，并为动态决策制定实验室减少问题提供了出色的解决方案。

结论

这项工作演示了一种机器学习模型，可以帮助医生确定哪些实验室测试可以省略。