The paper presents a Time-Aware transformer-based model designed to predict Acute Exacerbation of Chronic Obstructive Pulmonary Disease (AECOPD) using only respiratory data from daily-use ventilators. By capturing symptom patterns and their temporal progression, the model generates meaningful patient representations and outperforms traditional methods across multiple classification tasks. This approach aims to provide timely detection of AECOPD while minimizing latency associated with clinical and laboratory data.
By Weihao Qu, Ling Zheng, Dongyang Wang, Jiacun Wang, Haowen Pan
SepsisLens is a new model for early sepsis warning that preserves the structure of individual physiological signals while predicting risk. It uses observation-aware representations to encode each variable’s dynamics and a shared temporal encoder to maintain variable axes, then composes multi-horizon risk through a StructuredRiskHead that includes variable-level and organ-level components. The model was evaluated on three public ICU cohorts and one private-hospital cohort, achieving strong discrimination and lower alert burden at matched recall on MIMIC-IV, with ablations confirming the design choices.
By Yikun Ou, Wei Li
The paper introduces a response‑aware patient dynamics model that explicitly captures how respiratory support alters a patient’s short‑term physiological trajectory. By decomposing predicted physiological change into baseline dynamics and support‑associated deviations anchored to room‑air reference, the model updates latent patient states across a forecast horizon. Experiments on ICU cohorts from two institutions show that the model matches baseline trajectory predictions overall and consistently improves performance when physiological states are changing.
By Xiaolei Lu, Shamim Nemati
The paper introduces NECA, a model‑agnostic augmentation technique that retrieves similar patients from the training set and transforms their organ‑dysfunction event onset times into cumulative event‑curve features for multiple future horizons (6h, 12h, 24h, 42h). These features are concatenated with the original patient representation and fed to standard downstream predictors, leading to improved PR‑AUC and ROC‑AUC on PhysioNet 2012 and a MIMIC‑IV subset across several organ‑dysfunction tasks. Ablation studies show that clinically meaningful retrieval and horizon‑specific neighbor risk are the main drivers of performance gains, especially for neurological dysfunction events.
By Chenyang Wang, Jiawei Theresa Tang, Xinran Liu, Xinyu Lu, Xulei Yang
The paper introduces CALIBRA, a calibration-first multimodal temporal learning framework designed for transferable asthma‑risk forecasting across varying patient cohorts and sensor ecosystems. It processes multiple data streams—environmental, pulmonary, symptom, medication, wearable, and context—using dedicated recurrent encoders, a reliability‑conditioned gate, and gradient‑reversal training to mitigate cohort bias. CALIBRA employs a shrinkage‑based hierarchical logistic layer for probability calibration and split conformal prediction for abstention‑capable prediction sets, achieving competitive performance on a semi‑synthetic three‑cohort benchmark with controlled distribution shift.
By Taimoor Ahmad
This study develops a rolling day‑wise mortality prediction model for critically ill patients with acute kidney injury on continuous renal replacement therapy (CRRT) by incorporating minute‑level machine pressure waveforms alongside electronic health record (EHR) data. After cleaning the raw machine data—aligning it with therapy intervals, removing priming and downtime minutes, and denoising artifacts—the authors trained a transformer‑based stacked ensemble that fuses a sequence transformer with classical models using circuit‑instability features and clinical variables. In a multi‑center benchmark of 976 patients, the combined model achieved a one‑day mortality AUROC of 0.766, outperforming machine‑only (0.625) and EHR‑only (0.717) models, and SHAP analysis highlighted key pressure‑related features such as filter pressure, transmembrane pressure, and access‑to‑return difference.
whyItMatters":"By integrating previously discarded CRRT machine data, the study provides a continuous, real‑time risk signal that could enable earlier detection of patient deterioration and improve mortality prediction in a high‑risk population."
By Shehan Irteza Pranto, Joanna Yang, Joshua Lambert, Stuart L. Goldstein, Lili Chan, Girish N. Nadkarni, Tiago K. Colicchio, Javier A. Neyra, Jin Chen