Intensivists

A Statistical Approach for Modeling Irregular Multivariate Time Series with Missing Observations

Irregular multivariate time series with missing values present significant challenges for predictive modeling in domains such as healthcare. While deep learning approaches often focus on temporal interpolation or complex architectures to handle irregularities, we propose a simpler yet effective alternative: extracting time-agnostic summary statistics to eliminate the temporal axis. Our method comp...

Machine Intelligence-Driven Forecasting for ED Triage and Dynamic Hospital Patient Routing

Overcrowding of emergency departments (ED) is now a problem of global health care concern due to the increase in patients. Triage systems have been established for a considerable period. However, their reliability in choosing the appropriate patient and the level of service has undergone much scrutiny. In this paper, we describe a comprehensive machine learning framework aimed at predicting critic...

A multi-layered approach to elucidate mechanisms of physical function in response to rehabilitation in heart failure with preserved ejection fraction

Heart failure with preserved ejection fraction (HFpEF) is an increasingly common cause of morbidity and mortality in older adults that is driven by ca...

Formally Verifying and Explaining Sepsis Treatment Policies with COOL-MC

Safe and interpretable sequential decision-making is critical in healthcare, yet reinforcement learning (RL) policies for sepsis treatment optimizatio...

MILD: Multi-Intent Learning and Disambiguation for Proactive Failure Prediction in Intent-based Networking

In multi-intent intent-based networks, a single fault can trigger co-drift where multiple intents exhibit symptomatic KPI degradation, creating ambigu...

Development and Validation of the Intensive Documentation Index for ICU Mortality Prediction: A Temporal Validation Study

Background: Nursing documentation patterns may reflect patient acuity and clinical deterioration, yet their prognostic value remains underexplored. We...

Deep Learning-Based Missing Value Imputation for Heart Failure Data from MIMIC-III: A Comparative Study of DAE, SAITS, and MICE+LightGBM

Background: Inadequate data in electronic health records can create problems for clinical decision support systems and predictive modelling tools. ICU...

Benchmarking Large Language Models for Intensive Care Unit Clinical Decision Support: A Dual Safety Evaluation of 26 Models on Consumer Hardware

Background: Large Language Models (LLMs) show promise for clinical decision support in Intensive Care Units (ICU), but their safety and reliability re...

Interpretable machine learning model for predicting kidney failure among CAKUT children in multicenter large-scale study

Congenital anomalies of the kidney and urinary tract (CAKUT) are the leading cause of pediatric kidney failure, but predicting individual progression ...

Kernel-Based Learning of Chest X-ray Images for Predicting ICU Escalation among COVID-19 Patients

Kernel methods have been extensively utilized in machine learning for classification and prediction tasks due to their ability to capture complex non-...

From Robotics to Sepsis Treatment: Offline RL via Geometric Pessimism

Offline Reinforcement Learning (RL) promises the recovery of optimal policies from static datasets, yet it remains susceptible to the overestimation o...

M3: High-fidelity Text-to-Image Generation via Multi-Modal, Multi-Agent and Multi-Round Visual Reasoning

Generative models have achieved impressive fidelity in text-to-image synthesis, yet struggle with complex compositional prompts involving multiple con...

Self-Supervised Learning with a Multi-Task Latent Space Objective

Self-supervised learning (SSL) methods based on Siamese networks learn visual representations by aligning different views of the same image. The multi...

Enabling Real-Time Colonoscopic Polyp Segmentation on Commodity CPUs via Ultra-Lightweight Architecture

Early detection of colorectal cancer hinges on real-time, accurate polyp identification and resection. Yet current high-precision segmentation models ...

Genomic Signatures and Prediction of Clinical Severity in Klebsiella pneumoniae infections in a Multicenter Cohort

Klebsiella pneumoniae is a major causative agent of hospital-acquired infections worldwide, contributing substantially to morbidity, mortality, and he...

NPCNet: Navigator-Driven Pseudo Text for Deep Clustering of Early Sepsis Phenotyping

Sepsis is a heterogeneous syndrome. Identifying clinically distinct phenotypes may enable more precise treatment strategies. In recent years, many res...

clinTALL: machine learning-driven multimodal subtypeclassification and treatment outcome prediction in pediatric T-ALL

Background: Childhood T-lineage acute lymphoblastic leukemia (T-ALL) is an aggressive hematologic malignancy with poor prognosis. Differently from B-c...

On Safer Reinforcement Learning Policies for Sedation and Analgesia in Intensive Care

Pain management in intensive care usually involves complex trade-offs between therapeutic goals and patient safety, since both inadequate and excessiv...

Temporal Sepsis Modeling: a Fully Interpretable Relational Way

Sepsis remains one of the most complex and heterogeneous syndromes in intensive care, characterized by diverse physiological trajectories and variable...

Interpretable and backpropagation-free Green Learning for efficient multi-task echocardiographic segmentation and classification

Echocardiography is a cornerstone for managing heart failure (HF), with Left Ventricular Ejection Fraction (LVEF) being a critical metric for guiding ...