Electric vehicles (EVs) are key to sustainable mobility, yet their lithium-ion batteries (LIBs) degrade more rapidly under prolonged high states of charge (SOC). This can be mitigated by delaying full charging DFC until just before departure, which requires accurate prediction of user departure times. In this work, we propose Transformer-based real-time-to-event (TTE) model for accurate EV departure prediction. Our approach represents each day as a TTE sequence by discretizing time into grid-based tokens. Unlike previous methods primarily dependent on temporal dependency from historical patterns, our method leverages streaming contextual information to predict departures. Evaluation on a real-world study involving 93 users and passive smartphone data demonstrates that our method effectively captures irregular departure patterns within individual routines, outperforming baseline models. These results highlight the potential for practical deployment of the DFC algorithm and its contribution to sustainable transportation systems.

Acute kidney injury (AKI) presents a public health challenge with profound short and long-term morbidity and mortality. Early prediction and severity identification of AKI are crucial for improving clinical outcomes through timely interventions and efficient resource allocation. Previous studies have predominantly focused on serum creatinine, neglecting the significance of urine output, which, combined with the delayed rise in serum creatinine post-AKI onset, hinders the timely detection of AKI. To address these shortcomings, we propose a novel multi-task learning approach incorporating a continuous urine output monitoring strategy, predicting AKI onset and stage within 6-hour intervals up to 48 hours. Our model exhibits strong performance with area under the receiver operating characteristic curve (ROC) 99.3% and area under the precision-recall curve (PRC) 99.0% for predicting AKI within 48 hours, ROC 97.4% and PRC 98.6% for predicting renal replacement therapy for AKI patients. Also, our model is able to capture overall disease trends perfectly for 35.7% of the AKI cohort and 94.8% of the disease-free cohort. The proposed approach enhances clinical applicability, providing insights into disease dynamics.

Shock is a life-threatening condition characterized by generalized circulatory failure, which can have devastating consequences if not promptly treated. Thus, early prediction and continuous monitoring of physiological signs are essential for timely intervention. While previous machine learning research in clinical settings has primarily focused on predicting the onset of deteriorating events, the importance of monitoring the ongoing state of a patient's condition post-onset has often been overlooked. In this study, we introduce a novel analytical framework for a prognostic monitoring system that offers hourly predictions of shock occurrence within the next 8 hours preceding its onset, along with forecasts regarding the likelihood of shock continuation within the subsequent hour post-shock occurrence. We categorize the patient's physiological states into four cases: pre-shock (non-shock or shock within the next 8 hours) and post-shock onset (continuation or improvement of shock within the next hour). To effectively predict these cases, we adopt supervised contrastive learning, enabling differential representation in latent space for training a predictive model. Additionally, to extract effective contrastive embeddings, we incorporate a feature tokenizer transformer into our approach. Our framework demonstrates improved predictive performance compared to baseline models when utilizing contrastive embeddings, validated through both internal and external datasets. Clinically, our system significantly improved early detection by identifying shock on average 6 hours before its onset. This framework not only provides early predictions of shock likelihood but also offers real-time assessments of shock persistence risk, thereby facilitating early prevention and evaluation of treatment effectiveness.

Lithium plating poses a significant challenge to the performance and safety of lithium-ion batteries. As a non-destructive detection method, voltage relaxation profile (VRP) analysis shows great potential for effective lithium plating detection. However, the conventional VRP analysis suffers from a lengthy experimental time requirement, which fundamentally hinders the further development of the VRP-based detection. To overcome this limitation, this paper proposes a new lithium plating indicator by fully exploiting distinctive behaviors of differential voltage$(dV/dt)$profiles depending on the amount of lithium plating. The proposed indicator focuses on a local maximum value in the$dV/dt$profiles, which allows for achieving robust and fast prediction under cell-to-cell variation and aging while preserving the quantitative information obtained from the conventional detection. Based on experiments using battery cells, the adoption of the proposed indicator reduces the lithium plating detection time by 40% with a minimum error compared with the conventional method. Furthermore, applying the same approach to reference datasets further validates the efficacy of the proposed indicator across various conditions, including varying charging currents and temperatures, which confirms the reliability and accuracy of the proposed fast lithium plating detection.