Hospital-Based Medicine

ChronoFormer: Time-Aware Transformer Architectures for Structured Clinical Event Modeling

The temporal complexity of electronic health record (EHR) data presents significant challenges for predicting clinical outcomes using machine learning. This paper proposes ChronoFormer, an innovative transformer based architecture specifically designed to encode and leverage temporal dependencies in longitudinal patient data. ChronoFormer integrates temporal embeddings, hierarchical attention me...

StayLTC: A Cost-Effective Multimodal Framework for Hospital Length of Stay Forecasting

Accurate prediction of Length of Stay (LOS) in hospitals is crucial for improving healthcare services, resource management, and cost efficiency. This paper presents StayLTC, a multimodal deep learning framework developed to forecast real-time hospital LOS using Liquid Time-Constant Networks (LTCs). LTCs, with their continuous-time recurrent dynamics, are evaluated against traditional models usin...

On the Effectiveness and Generalization of Race Representations for Debiasing High-Stakes Decisions

Understanding and mitigating biases is critical for the adoption of large language models (LLMs) in high-stakes decision-making. We introduce Admiss...

Going beyond explainability in multi-modal stroke outcome prediction models

Aim: This study aims to enhance interpretability and explainability of multi-modal prediction models integrating imaging and tabular patient data. ...

Towards deployment-centric multimodal AI beyond vision and language

Multimodal artificial intelligence (AI) integrates diverse types of data via machine learning to improve understanding, prediction, and decision-mak...

Artificial intelligence in colorectal surgery multidisciplinary team approach-From innovation to application.

Artificial intelligence (AI) has played a novel role in aiding healthcare system functions and enhancing the patient experience. Multidisciplinary tea...

Machine Learning in Automated Food Processing: A Mini Review.

Industrial food processing is rapidly transforming into automation and digitalization. Automated food processing systems adapt to variations in raw ma...

Comparing representations of long clinical texts for the task of patient note-identification

In this paper, we address the challenge of patient-note identification, which involves accurately matching an anonymized clinical note to its corres...

TRACE: Intra-visit Clinical Event Nowcasting via Effective Patient Trajectory Encoding

Electronic Health Records (EHR) have become a valuable resource for a wide range of predictive tasks in healthcare. However, existing approaches hav...

Prediction of 30-day hospital readmission with clinical notes and EHR information

High hospital readmission rates are associated with significant costs and health risks for patients. Therefore, it is critical to develop predictive...

Leveraging LLMs for Predicting Unknown Diagnoses from Clinical Notes

Electronic Health Records (EHRs) often lack explicit links between medications and diagnoses, making clinical decision-making and research more diff...

Fine-Tuning LLMs on Small Medical Datasets: Text Classification and Normalization Effectiveness on Cardiology reports and Discharge records

We investigate the effectiveness of fine-tuning large language models (LLMs) on small medical datasets for text classification and named entity reco...

Semi-supervised learning for marine anomaly detection on board satellites

Aquatic bodies face numerous environmental threats caused by several marine anomalies. Marine debris can devastate habitats and endanger marine life...

Clean & Clear: Feasibility of Safe LLM Clinical Guidance

Background: Clinical guidelines are central to safe evidence-based medicine in modern healthcare, providing diagnostic criteria, treatment options...

Experience Retrieval-Augmentation with Electronic Health Records Enables Accurate Discharge QA

To improve the reliability of Large Language Models (LLMs) in clinical applications, retrieval-augmented generation (RAG) is extensively applied to ...

Limits of trust in medical AI

Artificial intelligence (AI) is expected to revolutionize the practice of medicine. Recent advancements in the field of deep learning have demonstra...

Validating Emergency Department Admission Predictions Based on Local Data Through MIMIC-IV

The effective management of Emergency Department (ED) overcrowding is essential for improving patient outcomes and optimizing healthcare resource al...

MAP: Evaluation and Multi-Agent Enhancement of Large Language Models for Inpatient Pathways

Inpatient pathways demand complex clinical decision-making based on comprehensive patient information, posing critical challenges for clinicians. De...

Developing and Evaluating an AI-Assisted Prediction Model for Unplanned Intensive Care Admissions following Elective Neurosurgery using Natural Language Processing within an Electronic Healthcare Record System

Introduction: Timely care in a specialised neuro-intensive therapy unit (ITU) reduces mortality and hospital stays, with planned admissions being sa...

[The application of domestic flexible arm robot in assisting transoral resection of throat tumors].

The current study aimed to evaluate the application value of the domestic otolaryngology-specific flexible-arm robotic system in the resection of thro...