AIMC Topic: Hospitalization
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Natural language processing assisted detection of inappropriate proton pump inhibitor use in adult hospitalised patients.
European journal of hospital pharmacy : science and practice
Aug 21, 2025
OBJECTIVES: To establish a clinical application monitoring system for proton pump inhibitors (PPI-MS) and to enhance the detection and intervention of inappropriate PPI use in adult hospitalised patients.
Effect of discontinuing antipsychotic medications on the risk of hospitalization in long-term care: a machine learning-based analysis.
BMC medicine
Aug 20, 2025
BACKGROUND: Antipsychotic medications are frequently prescribed to older residents of long-term care facilities (LTCFs) despite their limited efficacy and considerable safety risks. While discontinuation of these drugs might help reduce their associa...
Machine learning algorithms to predict the risk of admission to intensive care units in HIV-infected individuals: a single-centre study.
Virology journal
Aug 5, 2025
Antiretroviral therapy (ART) has transformed HIV from a rapidly progressive and fatal disease to a chronic disease with limited impact on life expectancy. However, people living with HIV(PLWHs) faced high critical illness risk due to the increased pr...
Predicting Emergency Severity Index (ESI) level, hospital admission, and admitting ward in an emergency department using data-driven machine learning.
BMC medical informatics and decision making
Jul 28, 2025
INTRODUCTION: Emergency departments (EDs) are critical for ensuring timely patient care, especially in triage, where accurate prioritisation is essential for patient safety and resource utilisation. Building on previous research, this study leverages...
Artificial intelligence platform to predict children's hospital care for respiratory disease using clinical, pollution, and climatic factors.
Journal of global health
Jul 21, 2025
BACKGROUND: Hospitals and health care systems may benefit from artificial intelligence (AI) and big data to analyse clinical information combined with external sources. Machine learning, a subset of AI, uses algorithms trained on data to generate pre...
Which explanations do clinicians prefer? A comparative evaluation of XAI understandability and actionability in predicting the need for hospitalization.
BMC medical informatics and decision making
Jul 16, 2025
BACKGROUND: This study aims to address the gap in understanding clinicians' attitudes toward explainable AI (XAI) methods applied to machine learning models using tabular data, commonly found in clinical settings. It specifically explores clinicians'...
Machine learning models predict risk of lower extremity deep vein thrombosis in hospitalized patients with spontaneous intracerebral hemorrhage.
Scientific reports
Jul 10, 2025
Lower extremity deep vein thrombosis is one of the important complications of spontaneous intracerebral hemorrhage. We aimed to develop a risk assessment model to predict the risk of lower extremity DVT during hospitalization in patients with spontan...
Inflammatory, fibrotic and endothelial biomarker profiles in COVID-19 patients during and following hospitalization.
Scientific reports
Jul 10, 2025
Survivors of severe COVID-19 often suffer from long-term respiratory issues, but the molecular drivers of this damage remain unclear. This study explored the dynamics of inflammatory, fibrotic, and endothelial biomarkers in hospitalized COVID-19 pati...
AI Predictive Model of Mortality and Intensive Care Unit Admission in the COVID-19 Pandemic: Retrospective Population Cohort Study of 12,000 Patients.
Journal of medical Internet research
Jul 10, 2025
BACKGROUND: One of the main challenges with COVID-19 has been that although there are known factors associated with a worse prognosis, clinicians have been unable to predict which patients, with similar risk factors, will die or require intensive car...
Development and Validation of a Rule-Based Natural Language Processing Algorithm to Identify Falls in Inpatient Records of Older Adults: Retrospective Analysis.
JMIR aging
Jul 8, 2025
BACKGROUND: In order to address fall underestimation by the International Classification of Diseases (ICD) in clinical settings, information from clinical notes could be incorporated via natural language processing (NLP) as a possible solution. Howev...
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