Hospital-Based Medicine

Latest AI and machine learning research in hospital-based medicine for healthcare professionals.

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Application of a machine learning model for early prediction of in-hospital cardiac arrests: Retrospective observational cohort study.

OBJECTIVE: To describe the results of the application of a Machine Learning (ML) model to predict in...

Machine Learning Model Reveals Determinators for Admission to Acute Mental Health Wards From Emergency Department Presentations.

This research addresses the critical issue of identifying factors contributing to admissions to acut...

From admission to discharge: a systematic review of clinical natural language processing along the patient journey.

BACKGROUND: Medical text, as part of an electronic health record, is an essential information source...

The Role of Artificial Intelligence on Tumor Boards: Perspectives from Surgeons, Medical Oncologists and Radiation Oncologists.

The integration of multidisciplinary tumor boards (MTBs) is fundamental in delivering state-of-the-a...

Enhanced machine learning approaches for OSA patient screening: model development and validation study.

Age, gender, body mass index (BMI), and mean heart rate during sleep were found to be risk factors f...

A deep learning classification framework for research methods of marine protected area management.

The latest emerging transdisciplinary marine protected area (MPA) research scheme requires efficient...

Multi-task heterogeneous graph learning on electronic health records.

Learning electronic health records (EHRs) has received emerging attention because of its capability ...

Investigation of emergency department abandonment rates using machine learning algorithms in a single centre study.

A critical problem that Emergency Departments (EDs) must address is overcrowding, as it causes exten...

Prediction of 90 day readmission in heart failure with preserved ejection fraction by interpretable machine learning.

AIMS: Certain critical risk factors of heart failure with preserved ejection fraction (HFpEF) patien...

Machine learning quantification of Amyloid-β deposits in the temporal lobe of 131 brain bank cases.

Accurate and scalable quantification of amyloid-β (Aβ) pathology is crucial for deeper disease pheno...

Machine learning-based identification of the risk factors for postoperative nausea and vomiting in adults.

Postoperative nausea and vomiting (PONV) is a common adverse effect of anesthesia. Identifying risk ...

Early sepsis mortality prediction model based on interpretable machine learning approach: development and validation study.

Sepsis triggers a harmful immune response due to infection, causing high mortality. Predicting sepsi...

Identification of medication-related fall risk in adults and older adults admitted to hospital: A machine learning approach.

The study aimed to develop and validate, through machine learning, a fall risk prediction model rela...

Predicting the trajectory of non-suicidal self-injury among adolescents.

BACKGROUND: Non-suicidal self-injury (NSSI) is common among adolescents receiving inpatient psychiat...

A study of "left against medical advice" emergency department patients: an optimized explainable artificial intelligence framework.

The issue of left against medical advice (LAMA) patients is common in today's emergency departments ...

How do large language models answer breast cancer quiz questions? A comparative study of GPT-3.5, GPT-4 and Google Gemini.

Applications of large language models (LLMs) in the healthcare field have shown promising results in...

Prediction of neurologic outcome after out-of-hospital cardiac arrest: An interpretable approach with machine learning.

UNLABELLED: Out-of-hospital cardiac arrest (OHCA) is a critical condition with low survival rates. I...

Multidisciplinary cancer disease classification using adaptive FL in healthcare industry 5.0.

Emerging Industry 5.0 designs promote artificial intelligence services and data-driven applications ...

Predicting whether patients in an acute medical unit are physiologically fit-for-discharge using machine learning: A proof-of-concept.

INTRODUCTION: Delays in discharging patients from Acute Medical Units hamper patient flows throughou...

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