Infection Control

Machine learning-based prediction of 90-day prognosis and in-hospital mortality in hemorrhagic stroke patients.

This study aims to predict hemorrhagic stroke outcomes, including 90-day prognosis and in-hospital mortality, using machine learning models and SHapley Additive exPlanations (SHAP) analysis. Data were collected from a national Stroke Registry from January 2014 to July 2022. Various predictive factors were considered, such as stroke severity at presentation, patient demographics, laboratory results...

Development and Validation of Comorbidity Severity Adjustment Methods in Mortality Models for Acute Cerebrovascular Disease Using Survival and Machine Learning Analyses.

: This study aimed to develop and validate comorbidity-based severity adjustment methods for acute cerebrovascular disease by recalibrating the Charlson Comorbidity Index (CCI) and constructing a CCS-based comorbidity index to improve mortality risk prediction. : Using the Korea Disease Control Agency's Discharge Injury In-depth Survey Dataset (2013-2022), we applied Cox proportional hazards regre...

Predicting onward care needs at admission to reduce discharge delay using explainable machine learning.

Early identification of patients who require onward referral to social care can prevent delays to discharge from hospital. We introduce an explainable...

Assessing the Impact of External and Internal Factors on Emergency Department Overcrowding

Study Objective: To analyze the factors influencing Emergency Department (ED) overcrowding by examining the impacts of operational, environmental, a...

Machine Learning and Statistical Insights into Hospital Stay Durations: The Italian EHR Case

Length of hospital stay is a critical metric for assessing healthcare quality and optimizing hospital resource management. This study aims to identi...

Early Detection of Multidrug Resistance Using Multivariate Time Series Analysis and Interpretable Patient-Similarity Representations

Background and Objectives: Multidrug Resistance (MDR) is a critical global health issue, causing increased hospital stays, healthcare costs, and mor...

Fed-NDIF: A Noise-Embedded Federated Diffusion Model For Low-Count Whole-Body PET Denoising

Low-count positron emission tomography (LCPET) imaging can reduce patients' exposure to radiation but often suffers from increased image noise and r...

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...

IoT Integration Protocol for Enhanced Hospital Care

This paper introduces the "IoT Integration Protocol for Enhanced Hospital Care", a comprehensive framework designed to leverage Internet of Things (...

A dataset and benchmark for hospital course summarization with adapted large language models.

OBJECTIVE: Brief hospital course (BHC) summaries are clinical documents that summarize a patient's hospital stay. While large language models (LLMs) d...

COVID-19 Patients Benefitting From Remdesivir for Improved Survival: A Neural Network-Based Approach.

Conflicting results from randomized trials regarding the efficacy of remdesivir for COVID-19 have been reported. We aimed to develop a neural network ...

[Construction and preliminary validation of machine learning predictive models for cervical cancer screening based on human DNA methylation].

Using methylation characteristics of human genes to construct machine learning predictive models for screening cervical cancer and precancerous lesio...

Machine learning algorithm approach to complete blood count can be used as early predictor of COVID-19 outcome.

Although the SARS-CoV-2 infection has established risk groups, identifying biomarkers for disease outcomes is still crucial to stratify patient risk a...

Foundation Model of Electronic Medical Records for Adaptive Risk Estimation

The U.S. allocates nearly 18% of its GDP to healthcare but experiences lower life expectancy and higher preventable death rates compared to other hi...

A Study on 5G Network Slice Isolation Based on Native Cloud and Edge Computing Tools

5G networks support various advanced applications through network slicing, network function virtualization (NFV), and edge computing, ensuring low l...

Predicting In-Hospital Fall Risk Using Machine Learning With Real-Time Location System and Electronic Medical Records.

BACKGROUND: Hospital falls are the most prevalent and fatal event in healthcare, posing significant risks to patient health outcomes and institutional...

A Hybrid Data-Driven Approach For Analyzing And Predicting Inpatient Length Of Stay In Health Centre

Patient length of stay (LoS) is a critical metric for evaluating the efficacy of hospital management. The primary objectives encompass to improve ef...

UniTrans: A Unified Vertical Federated Knowledge Transfer Framework for Enhancing Cross-Hospital Collaboration

Cross-hospital collaboration has the potential to address disparities in medical resources across different regions. However, strict privacy regulat...

MARISMa: a routine MALDI-TOF MS dataset from 2018 to 2024 from Spain

Clinical microbiology laboratories play a crucial role in identifying pathogens, guiding antibiotic treatment, and managing antimicrobial resistance (...

Modeling trajectories of routine blood tests as dynamic biomarkers for outcome in spinal cord injury

Early outcome prediction after acute traumatic spinal cord injury (SCI) is challenging due to pathological complexities and population heterogeneity. ...