COPD

Whole-Body MRI Classification via Prompt-Based Clinical Conditioning

Combining whole-body magnetic resonance imaging (WB-MRI) with clinical variables has the potential to improve systemic disease diagnosis by leveraging complementary sources of patient information. However, structured clinical variables are often incomplete or missing, limiting the applicability of conventional multimodal fusion methods that assume fixed inputs. In this work, we propose TACTIC (Tab...

Beyond Padua and IMPROVE: Machine Learning Outperforms Guideline Risk Scores for Prediction of Radiologically Confirmed Hospital-Acquired Venous Thromboembolism

*Background:** Hospital-acquired venous thromboembolism (VTE) is a leading preventable cause of in-hospital morbidity and mortality. Guideline-endorsed risk scores (Padua, IMPROVE) achieve only moderate discrimination in unselected hospital-wide cohorts. **Methods:** We analyzed 399,624 adult admissions in MIMIC-IV (2008-2022), excluding admissions with prior VTE to restrict the cohort to first-ev...

MT-LLE: Multi-Task Locally Linear Embedding for Interpretable Disease Modeling from Longitudinal Omics Data

Constructing interpretable disease models from longitudinal omics data is a central challenge in precision medicine. The goal is a low-dimensional rep...

LUTSeg: A Longitudinal Multi-Expert Dataset for Ulcer Tissue Segmentation

Quantifying wound tissue composition is essential for monitoring chronic ulcer progression and guiding treatment decisions. However, pixel-level annot...

Time-Aware Tranformer-Based Prediction Model for AECOPD

The rapid symptom change of Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) makes it critical to have time-sensitive prediction m...

A Two-Stage Time-Aware Transformer for Short-Horizon AECOPD Risk Prediction

Acute exacerbation of chronic obstructive pulmonary disease (AECOPD) can worsen rapidly, making timely prediction a clinical priority. Most existing m...

A Vision-Language Framework for Predicting Brain Tumor Recurrence from Multimodal, Longitudinal Patient Data

Accurate prediction of tumor recurrence in brain tumor patients following surgery is essential for optimizing adjuvant therapy, response assessment, a...

Rubric Dropout: A Simple Way to Mitigate Reward Hacking in Rubric-as-Reward RL

Reinforcement learning against rubrics, lists of criteria graded by an LLM judge, has become a standard way to post-train language models on tasks wit...

A membrane-impermeant nucleic acid dye converts bacteriophage plaque assays into a machine-readable format for automated counting

Plaque assays remain the gold standard for bacteriophage quantification, but routine plaque counting is labor-intensive, time-consuming, and poorly su...

MIRA: an open source and user-friendly software to automate counting and sizing of fungal spores

Background The quantification of fungal spores constitutes a fundamental metric in phytopathology, serving as the primary variable for inoculum standa...

Keyphrase Identification Using Minimal Labeled Data with Hierarchical Contexts and Transfer Learning

Background: Interoperable clinical decision support system (CDSS) rules provide a pathway to interoperability, a well-recognized challenge in health i...

Patient-Level Risk Characterization of Drug-Associated Hidradenitis Suppurativa Using Machine Learning

Hidradenitis suppurativa (HS) is a chronic, debilitating, inflammatory skin disorder. Medications have been reported in association with cases of new-...

The Dark Room in the Reward Channel: Dense Prediction Rewards Collapse GRPO-Trained LLM Agents -- and What Actually Works

Dense per-step supervision is an appealing remedy for sparse-reward, long-horizon LLM agents: reward the agent for predicting its next observation, an...

Interaction-finder: automated literature-based discovery of biological entity associations with quote-level provenance

Identifying interactions between biological entities is a cornerstone of molecular research, but assembling such lists from the literature is slow and...

False Confidence: Automated Labels Confound Fairness Audits in Cervical Spine Segmentation

Automated segmentation of cervical-spine MRI is increasingly used in clinical workflows, yet no fairness audit exists for this anatomy. We show that a...

MIBE: Multi-subject Interaction Benchmark and Evaluator for Personalized Image Generation

Multi-subject personalized image generation requires the precise rendering of all requested reference identities and their specified interactions base...

OHCA-EXTRACT: Evaluating the Accuracy of a Large Language Model Pipeline for Out-of-Hospital Cardiac Arrest Case Identification and Utstein Variable Extraction

Background: Manual identification and abstraction of out-of-hospital cardiac arrest (OHCA) cases and Utstein template variables from electronic health...

NLP Framework for Automated Symptom Severity Staging in Heart Failure and COPD Clinical Notes Using Ontology Integration: A Study Protocol

Background: Heart failure (HF) and chronic obstructive pulmonary disease (COPD) are among the leading causes of morbidity and mortality globally, with...

Automated EEG Classification to Track Levels of Consciousness

Precise prognostication in acute brain injury is limited by a lack of reliable biomarkers of consciousness available to clinicians at the bedside. The...

Automated Segmentation of Prostatic Gold Fiducial Markers for MR-Only Radiotherapy Planning Using Multi-Modal Consensus Deep Learning

Purpose: To develop and evaluate a multi-model consensus deep learning approach for automated gold fiducial marker (FM) segmentation in T1-weighted pr...