文章目录
介绍
由于其灵活性和优越的性能，机器学习模型经常补充并优于传统的统计生存模型。然而，由于缺乏用户友好的工具来解释其内部操作和预测原理，它们的广泛采用受到阻碍。为了解决这个问题，我们引入了survex R包，它提供了一个内聚框架，通过应用可解释的人工智能技术来解释任何生存模型。所提出的软件的功能包括理解和诊断生存模型，这可以导致它们的改进。通过揭示决策过程的洞察力，例如变量效应和重要性，survex能够评估模型的可靠性和检测偏差。因此，可以在诸如生物医学研究和保健应用等敏感领域促进透明度和责任。
Due to their flexibility and superior performance, machine learning models frequently complement and outperform traditional statistical survival models. However, their widespread adoption is hindered by a lack of user-friendly tools to explain their internal operations and prediction rationales. To tackle this issue, we introduce the survex R package, which provides a cohesive framework for explaining any survival model by applying explainable artificial intelligence techniques. The capabilities of the proposed software encompass understanding and diagnosing survival models, which can lead to their improvement. By revealing insights into the decision-making process, such as variable effects and importances, survex enables the assessment of model reliability and the detection of biases. Thus, transparency and responsibility may be promoted in sensitive areas, such as biomedical research and healthcare applications.
代码
案例
r
library(survex)
library(survival)
library(ranger)
vet <- survival::veteran
cph <- coxph(Surv(time, status) ~ ., data = vet, x = TRUE, model = TRUE)
exp <- explain(cph, data = vet[, -c(3,4)], y = Surv(vet$time, vet$status))
#> Preparation of a new explainer is initiated
#> -> model label : coxph ( default )
#> -> data : 137 rows 6 cols
#> -> target variable : 137 values ( 128 events and 9 censored )
#> -> times : 50 unique time points , min = 1.5 , median survival time = 80 , max = 999
#> -> times : ( generated from y as uniformly distributed survival quantiles based on Kaplan-Meier estimator )
#> -> predict function : predict.coxph with type = 'risk' will be used ( default )
#> -> predict survival function : predictSurvProb.coxph will be used ( default )
#> -> predict cumulative hazard function : -log(predict_survival_function) will be used ( default )
#> -> model_info : package survival , ver. 3.7.0 , task survival ( default )
#> A new explainer has been created!
shap <- model_survshap(exp, veteran[c(1:4, 17:20, 110:113, 126:129), -c(3,4)])
plot(shap)
参考
- survex: an R package for explaining machine learning survival models