AIMC Topic: Hospitalization
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Identification of heart failure subtypes using transformer-based deep learning modelling: a population-based study of 379,108 individuals.
EBioMedicine
Mar 19, 2025
BACKGROUND: Heart failure (HF) is a complex syndrome with varied presentations and progression patterns. Traditional classification systems based on left ventricular ejection fraction (LVEF) have limitations in capturing the heterogeneity of HF. We a...
Construction of a machine learning-based interpretable prediction model for acute kidney injury in hospitalized patients.
Scientific reports
Mar 18, 2025
In this observational study, we used data from 59,936 hospitalized adults to construct a model. For the models constructed with all 53 variables, all five models achieved acceptable performance with the validation cohort, with the extreme gradient bo...
Machine Learning Predicts Bleeding Risk in Atrial Fibrillation Patients on Direct Oral Anticoagulant.
The American journal of cardiology
Feb 25, 2025
Predicting major bleeding in nonvalvular atrial fibrillation (AF) patients on direct oral anticoagulants (DOACs) is crucial for personalized care. Alternatives like left atrial appendage closure devices lower stroke risk with fewer nonprocedural blee...
Prediction of 90 day mortality in elderly patients with acute HF from e-health records using artificial intelligence.
ESC heart failure
Feb 13, 2025
AIMS: Mortality risk after hospitalization for heart failure (HF) is high, especially in the first 90 days. This study aimed to construct a model automatically predicting 90 day post-discharge mortality using electronic health record (EHR) data 48 h ...
A risk prediction model for venous thromboembolism in hospitalized patients with thoracic trauma: a machine learning, national multicenter retrospective study.
World journal of emergency surgery : WJES
Feb 13, 2025
BACKGROUND: Early treatment and prevention are the keys to reducing the mortality of VTE in patients with thoracic trauma. This study aimed to develop and validate an automatic prediction model based on machine learning for VTE risk screening in pati...
Artificial intelligence (AI) in pharmacovigilance: A systematic review on predicting adverse drug reactions (ADR) in hospitalized patients.
Research in social & administrative pharmacy : RSAP
Feb 12, 2025
INTRODUCTION: Adverse drug reactions (ADRs) significantly impact healthcare systems, leading to increased hospitalization rates and costs. With the growing adoption of artificial intelligence (AI) in healthcare, machine learning (ML) models offer pro...
Unsupervised machine learning clustering approach for hospitalized COVID-19 pneumonia patients.
BMC pulmonary medicine
Feb 8, 2025
BACKGROUND: Identification of distinct clinical phenotypes of diseases can guide personalized treatment. This study aimed to classify hospitalized COVID-19 pneumonia subgroups using an unsupervised machine learning approach.
A prospective real-time transfer learning approach to estimate influenza hospitalizations with limited data.
Epidemics
Feb 7, 2025
Accurate, real-time forecasts of influenza hospitalizations would facilitate prospective resource allocation and public health preparedness. State-of-the-art machine learning methods are a promising approach to produce such forecasts, but they requir...
Prediction of the Risk of Adverse Clinical Outcomes with Machine Learning Techniques in Patients with Noncommunicable Diseases.
Journal of medical systems
Feb 3, 2025
Decision-making in chronic diseases guided by clinical decision support systems that use models including multiple variables based on artificial intelligence requires scientific validation in different populations to optimize the use of limited human...
Identification of patient demographic, clinical, and SARS-CoV-2 genomic factors associated with severe COVID-19 using supervised machine learning: a retrospective multicenter study.
BMC infectious diseases
Jan 28, 2025
BACKGROUND: Drivers of COVID-19 severity are multifactorial and include multidimensional and potentially interacting factors encompassing viral determinants and host-related factors (i.e., demographics, pre-existing conditions and/or genetics), thus ...
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