Infection Control

Characterization Of Diseases In Temporal Comorbidity Networks

Comorbidity networks, which capture disease-disease co-occurrence usually based on electronic health records, reveal structured patterns in how diseases cluster and progress across individuals. However, how these networks evolve across different age groups and how this evolution relates to properties like disease prevalence and mortality remains understudied. To address these issues, we used pub...

Abstract Meaning Representation for Hospital Discharge Summarization

The Achilles heel of Large Language Models (LLMs) is hallucination, which has drastic consequences for the clinical domain. This is particularly important with regards to automatically generating discharge summaries (a lengthy medical document that summarizes a hospital in-patient visit). Automatically generating these summaries would free physicians to care for patients and reduce documentation...

Postoperative self-care ability of continuous nursing based on artificial intelligence for stroke patients with neurological injury.

According to the statistics of relevant data, stroke is a relatively common cerebrovascular disease, and its incidence rate is as high as 185/100,000 ...

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 fo...

A predictive model for hospital death in cancer patients with acute pulmonary embolism using XGBoost machine learning and SHAP interpretation.

The prediction of in-hospital mortality in cancer patients with acute pulmonary embolism (APE) remains a significant clinical challenge. This study ai...

Forecasting Surgical Bed Utilization: Architectural Design of a Machine Learning Pipeline Incorporating Predicted Length of Stay and Surgical Volume.

The objective of this study was to develop a machine learning model utilizing data from the electronic health record (EHR) to model length of stay and...

Early Prediction of In-Hospital ICU Mortality Using Innovative First-Day Data: A Review

The intensive care unit (ICU) manages critically ill patients, many of whom face a high risk of mortality. Early and accurate prediction of in-hospi...

Predicting hospital outpatient volume using XGBoost: a machine learning approach.

Hospital outpatient volume is influenced by a variety of factors, including environmental conditions and healthcare resource availability. Accurate pr...

Machine Learning Models Predicting Hospital Admissions During Chemotherapy Utilising Longitudinal Symptom Severity Reports and Patient-Reported Outcome Measures.

Chemotherapy toxicity can lead to acute hospital admissions, negatively impacting the healthcare system and patients' well-being. Machine learning (ML...

Transforming Data from a Commercial Hospital Information System into FHIR.

The expanded use of hospital information systems in recent decades offers possibilities to use data collected in the clinical routine not only for ind...

Machine Learning-Based Hospital Readmission Prediction: A Comparative Analysis of Speciality-Specific vs. All-Specialities Models.

Hospital readmissions are a major challenge for healthcare systems, leading to increased costs and adverse patient outcomes. Predicting which patients...

Improving prediction accuracy of hospital arrival vital signs using a multi-output machine learning model: a retrospective study of JSAS-registry data.

BACKGROUND: Critically ill patients can deteriorate rapidly; therefore, prompt prehospital interventions and seamless transition to in-hospital care u...

Identifying Mild-to-Moderate Atopic Dermatitis Using a Generic Machine Learning Approach: A Danish National Health Register Study.

Atopic dermatitis is a chronic skin disease, causing itching and recurrent eczematous lesions. In Danish national register data, adults with atopic de...

[Surveillance for pneumonia of unknown etiology: current status, challenges, and implementation ways].

The prevention and control of emerging and reemerging infectious diseases are crucial for national biosecurity, and surveillance and reporting of pneu...

Are Wearable ECG Devices Ready for Hospital at Home Application?

The increasing focus on improving care for high-cost patients has highlighted the potential of Hospital at Home (HaH) and remote patient monitoring (R...

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 SHaple...

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 Charls...

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...