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
INTERVenE introduces Transformer models that use a knowledge‑based temporal abstraction (KBTA) token stream of named clinical concepts instead of raw measurements, enabling per‑token attributions to resolve directly to clinical concepts. Two variants are offered: an auto‑regressive decoder that generates future abstraction trajectories with step‑wise risk readouts, and a bidirectional encoder that jointly predicts risk and time‑to‑event in a single pass. On 57,078 MIMIC‑IV admissions, the encoder variant outperforms neural baselines with a support‑weighted AUPRC of 0.672 and AUROC of 0.901, while the decoder provides complementary token‑level risk trajectories.
By Shahar Oded, Yuval Shahar
arXiv:2602. 00541v2 Announce Type: replace Abstract: Clinical events captured in Electronic Health Records (EHR) are irregularly sampled and may consist of a mixture of discrete events and numerical measurements, such as laboratory values or treatment dosages.
By Zilin Jing, Vincent Jeanselme, Yuta Kobayashi, Simon A. Lee, Chao Pang, Aparajita Kashyap, Yanwei Li, Xinzhuo Jiang, Shalmali Joshi
arXiv:2605. 11130v4 Announce Type: replace-cross Abstract: Critical events in multivariate time series, from turbine failures to cardiac arrhythmias, demand accurate prediction, yet labeled data is scarce because such events are rare and costly to annotate.
By Jonas Petersen, Gian-Alessandro Lombardi, Riccardo Maggioni, Camilla Mazzoleni, Federico Martelli, Philipp Petersen
Mr.Dec is a new Transformer‑decoder model that predicts 30‑day hospital readmission by treating each admission as a chronological sequence of daily multimodal events, integrating Electronic Health Record updates and Chest X‑ray findings. It uses disease‑specific supervised contrastive learning to shape a diagnosis‑aware latent space and preserves day‑level clinical signals that other methods often compress. Experiments on MIMIC‑IV and MIMIC‑CXR datasets show state‑of‑the‑art performance and the model can highlight "Critical Days" for actionable real‑time risk stratification.
By Minjun Kim, Jong Hak Moon
PGP-Clinical-TimeKAN is a trajectory-first framework for joint probabilistic forecasting of multivariate physiological data, combining missingness-aware temporal encoders, a soft organ-system prior, patient-specific relations, nonlinear Kolmogorov‑Arnold messages, and a low‑rank multivariate Student‑t head. Evaluated on a MIMIC‑IV cohort of 6,882 patients, it achieves the second‑lowest normalized MAE and the lowest RMSE among 13 models, while providing calibrated probabilistic forecasts with empirical coverage at 50%, 80%, and 95% intervals. Ablation studies show that relational structure is critical for performance, and increasing covariance rank improves likelihood but not point accuracy.
whyItMatters":"The model demonstrates that joint trajectory forecasting can yield highly accurate, calibrated predictions of physiological trajectories, offering a potentially inspectable intermediate task for clinical deterioration prediction."
By Weizhi Nie, Rihao Chang, Weijie Wang, Yuting Su
arXiv:2607. 23412v1 Announce Type: new Abstract: Electrocardiograms (ECGs) are widely used for cardiovascular risk prediction, yet models often fail to transfer across hospitals because of protocol, population, and measurement differences.
By Jie Lin, Weijie Sun, Sunil V. Kalmady, Anita Khalafbeigi, Abram Hindle, Padma Kaul, Russell Greiner