# Enhancing Survival Analysis Model Selection through XAI(t) in Healthcare
## 4 Discussion  
### Related Works  
In the artificial intelligence field, results are difficult to interpret, especially when dealing with deep models that are considered \"black boxes\", making it difficult to understand how the model generates predictions. XAI has recently been adapted to this context to improve the explainability, interpretability, and transparency of model results [47; 48; 49]. The goal of XAI algorithms is to convert unexplained ML and DL predictions into more interpretable \"white-box\" glass ones. Notably, in the realm of survival analysis, understanding which characteristics significantly influence predictions--essentially, understanding how such features are \"weighted\" for prognosis or diagnosis--is fundamental because it allows clinicians to choose the type of treatments for patients (preventive or curative). Most existing works apply XAI techniques like SHAP and LIME to AI-based frameworks addressing SA. [PERSON] et al. [50] used SHAP to improve the explainability of an ML-based framework concerning the role of mitochondrial regulatory genes in the evolution of renal clear cell carcinoma. [PERSON] et al. [13] adopted approximated SHAP values to build an interpretable transcriptomics-based prognostic system for Diffuse Large B-Cell Lymphoma  
Figure 5: Comparison of time-variant models: CPH, Cox time, and log hazard models. The x-axis represents the event time expressed in months, where each tick represents the event (black—0; red—1).  
(DLBCL). [PERSON] and [PERSON] [22] embedded SHAP and LIME in ML- and DL-based frameworks to support the prediction of survival rates for patients with pancreatic cancer. [PERSON] et al. [51] employed both SHAP and LIME to explain the survival predictions of many ML methods fed with a radiomics feature set extracted from tomographic images. [PERSON] et al. [23] used SHAP and LIME to explain ML-based estimations concerning the survival probability of children after bone marrow transplantation. Both SHAP and LIME were included in [PERSON]'s study [21] to better interpret the predictions of ML algorithms addressing SA on clinical data from people with nasopharyngeal carcinoma. [PERSON] and colleagues [24] used SHAP and LIME to corroborate the outcomes of ML models concerning hepatitis diagnosis and prognosis. Even fewer works have employed either SurvSHAP or SurvLIME for performing SA within ML-based workflows. [PERSON] and colleagues [12] employed SurvSHAP to investigate the efficacy of adjuvant chemotherapy starting from predictions of both ML and DL models concerning the survival probability of breast cancer patients. [PERSON] et al. [20] explained the outcomes of survival models fed by demographic and clinical features using both SurvSHAP(t) and SurvLIME. These XAI methods were oriented toward global and local explanations by analyzing data from the whole cohort or a single patient. [PERSON] et al. [19] performed SA to estimate hospitalization times by training ML algorithms on a multimodal dataset and explaining the results of time-to-event models for single patients using SurvSHAP. Remarkably, as far as we know, no studies have utilized XAI(t) to elucidate the predictions generated by a Deep Learning model. In fact, such architectures often play a supporting role in SA workflows; they are used not to predict mortality risks but to determine additional metrics that are then fed into a classical survival pipeline for performing SA [2,4,14]. In addition, these works did not include XAI strategies to better interpret how the model generated survival predictions. Related works in the literature show a paucity of studies on survival analysis with either ML algorithms or DL models embedding XAI techniques, e.g., SurvSHAP and SurvLIME, to increase the interpretability of the predictions of mortality risk. Table 3 summarizes related works involving classical XAI and XAI(t) in SA.