Critical Care

Machine learning models for predicting in-hospital mortality from acute pancreatitis in intensive care unit.

BACKGROUND: Acute pancreatitis (AP) represents a critical medical condition where timely and precise prediction of in-hospital mortality is crucial for guiding optimal clinical management. This study focuses on the development of advanced machine learning (ML) models to accurately predict in-hospital mortality among AP patients admitted to intensive care unit (ICU).

Visualized Text-to-Image Retrieval

We propose Visualize-then-Retrieve (VisRet), a new paradigm for Text-to-Image (T2I) retrieval that mitigates the limitations of cross-modal similarity alignment of existing multi-modal embeddings. VisRet first projects textual queries into the image modality via T2I generation. Then, it performs retrieval within the image modality to bypass the weaknesses of cross-modal retrievers in recognizing...

Multi-modal brain encoding models for multi-modal stimuli

Despite participants engaging in unimodal stimuli, such as watching images or silent videos, recent work has demonstrated that multi-modal Transform...

MedDreamer: Model-Based Reinforcement Learning with Latent Imagination on Complex EHRs for Clinical Decision Support

Timely and personalized treatment decisions are essential across a wide range of healthcare settings where patient responses vary significantly and ...

Cross-Sequence Semi-Supervised Learning for Multi-Parametric MRI-Based Visual Pathway Delineation

Accurately delineating the visual pathway (VP) is crucial for understanding the human visual system and diagnosing related disorders. Exploring mult...

MT$^{3}$: Scaling MLLM-based Text Image Machine Translation via Multi-Task Reinforcement Learning

Text Image Machine Translation (TIMT)-the task of translating textual content embedded in images-is critical for applications in accessibility, cros...

DoctorAgent-RL: A Multi-Agent Collaborative Reinforcement Learning System for Multi-Turn Clinical Dialogue

Large language models (LLMs) have demonstrated excellent capabilities in the field of biomedical question answering, but their application in real-w...

Supervised Machine Learning Models for Predicting Sepsis-Associated Liver Injury in Patients With Sepsis: Development and Validation Study Based on a Multicenter Cohort Study.

BACKGROUND: Sepsis-associated liver injury (SALI) is a severe complication of sepsis that contributes to increased mortality and morbidity. Early iden...

ORAKLE: Optimal Risk prediction for mAke30 in patients with sepsis associated AKI using deep LEarning.

BACKGROUND: Major Adverse Kidney Events within 30 days (MAKE30) is an important patient-centered outcome for assessing the impact of acute kidney inju...

Medical Large Vision Language Models with Multi-Image Visual Ability

Medical large vision-language models (LVLMs) have demonstrated promising performance across various single-image question answering (QA) benchmarks,...

Development of Interactive Nomograms for Predicting Short-Term Survival in ICU Patients with Aplastic Anemia

Aplastic anemia is a rare, life-threatening hematologic disorder characterized by pancytopenia and bone marrow failure. ICU admission in these patie...

DanmakuTPPBench: A Multi-modal Benchmark for Temporal Point Process Modeling and Understanding

We introduce DanmakuTPPBench, a comprehensive benchmark designed to advance multi-modal Temporal Point Process (TPP) modeling in the era of Large La...

Predicting Length of Stay in Neurological ICU Patients Using Classical Machine Learning and Neural Network Models: A Benchmark Study on MIMIC-IV

Intensive care unit (ICU) is a crucial hospital department that handles life-threatening cases. Nowadays machine learning (ML) is being leveraged in...

Multi-Person Interaction Generation from Two-Person Motion Priors

Generating realistic human motion with high-level controls is a crucial task for social understanding, robotics, and animation. With high-quality MO...

Relationship between medication regimen complexity and pharmacist engagement in fluid stewardship.

PURPOSE: The medication regimen complexity intensive care unit (MRC-ICU) score has previously been associated with pharmacist workload and fluid overl...

Multi-view contrastive learning and symptom extraction insights for medical report generation.

The task of generating medical reports automatically is of paramount importance in modern healthcare, offering a substantial reduction in the workload...

Multi-SpatialMLLM: Multi-Frame Spatial Understanding with Multi-Modal Large Language Models

Multi-modal large language models (MLLMs) have rapidly advanced in visual tasks, yet their spatial understanding remains limited to single images, l...

DetailMaster: Can Your Text-to-Image Model Handle Long Prompts?

While recent text-to-image (T2I) models show impressive capabilities in synthesizing images from brief descriptions, their performance significantly...

RBench-V: A Primary Assessment for Visual Reasoning Models with Multi-modal Outputs

The rapid advancement of native multi-modal models and omni-models, exemplified by GPT-4o, Gemini, and o3, with their capability to process and gene...

Enhancing healthcare AI stability with edge computing and machine learning for extubation prediction.

The advancement of the Internet of Medical Things (IoMT) has revolutionized data acquisition and processing in critical care settings. Given the pivot...