School of Mathematics, Southeast University, Nanjing 211189, China.
School of Automation, Southeast University, Nanjing 210096, China.
J Healthc Eng. 2022 Mar 24;2022:7892408. doi: 10.1155/2022/7892408. eCollection 2022.
In the intensive care unit, the monitored variables collected from sensors may have different behaviors among patients with different clinical basic information. Giving prior information of the monitored variables based on their specific basic information as soon as the patient is admitted will support the clinicians with better decisions during the surgery. Instead of black box models, the explainable hidden Markov model is proposed, which can estimate the possible distribution parameters of the monitored variables under different clinical basic information. A Student's -test or correlation test is conducted further to test whether the parameters have a significant relationship with the basic variables. The specific relationship is explored by using a conditional inference tree, which is an explainable model giving deciding rules. Instead of point estimation, interval forecast is chosen as the performance metrics including coverage rate and relative interval width, which provide more reliable results. By applying the methods to an intensive care unit data set with more than 20 thousand patients, the model has good performance with an area under the ROC Curve value of 0.75, which means the hidden states can generally be correctly labelled. The significant test shows that only a few combinations of the basic and monitored variables are not significant under the 0.01 significant level. The tree model based on different quantile intervals provides different coverage and width combination choices. A coverage rate around 0.8 is suggested, which has a relative interval width of 0.77.
在重症监护病房中，从传感器收集的监测变量在具有不同临床基本信息的患者中可能具有不同的行为。一旦患者入院，根据其特定的基本信息提供监测变量的先验信息，将支持临床医生在手术过程中做出更好的决策。与黑盒模型不同，提出了可解释的隐马尔可夫模型，该模型可以估计在不同临床基本信息下监测变量的可能分布参数。然后进一步进行学生 t 检验或相关检验，以测试参数是否与基本变量有显著关系。通过使用条件推断树（一种提供决策规则的可解释模型）探索具体关系，该树是一种可解释模型，可提供决策规则。与点估计不同，选择区间预测作为性能指标，包括覆盖率和相对区间宽度，从而提供更可靠的结果。通过将该方法应用于一个包含超过 2 万名患者的重症监护病房数据集，该模型具有良好的性能，ROC 曲线下面积值为 0.75，这意味着隐藏状态通常可以正确标记。显著检验表明，只有少数基本变量和监测变量的组合在 0.01 的显著水平下不显著。基于不同分位数区间的树模型提供了不同的覆盖率和宽度组合选择。建议覆盖率约为 0.8，相对区间宽度为 0.77。
