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Deep learning models have demonstrated impressive accuracy in predicting acute kidney injury (AKI), a condition affecting up to 20% of ICU patients, yet their black-box nature prevents clinical adoption in high-stakes critical care settings. While existing interpretability methods like SHAP, LIME, and attention mechanisms can identify important features, they fail to capture the temporal dynamics essential for clinical decision-making, and are unable to communicate when specific risk factors become critical in a patient's trajectory. This limitation is particularly problematic in the ICU, where the timing of interventions can significantly impact patient outcomes. We present a novel interpretable framework that brings temporal awareness to deep learning predictions for AKI. Our approach introduces three key innovations: (1) a latent convolutional concept bottleneck that learns clinically meaningful patterns from ICU time-series without requiring manual concept annotation, leveraging Conv1D layers to capture localized temporal patterns like sudden physiological changes; (2) Temporal Concept Tracing (TCT), a gradient-based method that identifies not only which risk factors matter but precisely when they become critical addressing the fundamental question of temporal relevance missing from current XAI techniques; and (3) integration with MedAlpaca to generate structured, time-aware clinical explanations that translate model insights into actionable bedside guidance. We evaluate our framework on MIMIC-IV data, demonstrating that our approach performs better than existing explainability frameworks, Occlusion and LIME, in terms of the comprehensiveness score, sufficiency score, and processing time. The proposed method also better captures risk factors inflection points for patients timelines compared to conventional concept bottleneck methods, including dense layer and attention mechanism. This work represents the first comprehensive solution for interpretable temporal deep learning in critical care that addresses both the what and when of clinical risk factors. By making AKI predictions transparent and temporally contextualized, our framework bridges the gap between model accuracy and clinical utility, offering a path toward trustworthy AI deployment in time-sensitive healthcare settings. 

We present and compare three approaches for accurately retrieving depth-resolved temperature distributions within materials from their thermal-radiation spectra, based on: (1) a nonlinear equation solver implemented in commercial software, (2) a custom-built nonlinear equation solver, and (3) a deep neural network (DNN) model. These methods are first validated using synthetic datasets comprising randomly generated temperature profiles and corresponding noisy thermal-radiation spectra for three different structures: a fused-silica substrate, an indium antimonide substrate, and a thin-film gallium nitride layer on a sapphire substrate. We then assess the performance of each approach using experimental spectra collected from a fused-silica window heated on a temperature-controlled stage. Our results demonstrate that the DNN-based method consistently outperforms conventional numerical techniques on both synthetic and experimental data, providing a robust solution for accurate depth-resolved temperature profiling.