BACKGROUND: Cardiac arrest (CA), characterized by an extremely high mortality rate, remains one of the most pressing global public health challenges. It not only causes a substantial strain on health care systems but also severely impacts individual ...
INTRODUCTION: Visual Patient Predictive (VPP) is an AI-based extension of the Visual Patient Avatar (VPA) that integrates deep learning models to predict upcoming vital sign deviations and display them as dashed visual elements. By explicitly showing...
Standard episodic patient monitoring of vital signs on the medical-surgical wards can potentially miss changes in health status and delay recognition of risk. To reduce these delays, we develop a clinical wearable-based deep learning model, using 9 i...
The existing literature lacks a comprehensive analysis of the clinical evolution of septic patients, which is highly heterogeneous and patient-dependent. The aim of this study is to develop machine learning models capable of predicting the clinical e...
BACKGROUND: Traumatic brain injury (TBI) is a critically ill disease with a high mortality rate, and clinical treatment is committed to continuously optimizing treatment strategies to improve survival rates.
BACKGROUND: Postoperative acute kidney injury (PO-AKI) prediction models for non-cardiac major surgeries typically rely solely on preoperative clinical characteristics.
The study aims to assess the efficacy of various neural network architectures in predicting the National Early Warning Systems (NEWS) score, using vital signs, to enhance early warning and monitoring in clinical settings. A comparative evaluation o...
PURPOSE: To use machine learning to predict new-onset shock for at-risk intensive care unit (ICU) patients based on discrete vital sign data from the electronic health record.
The study aimed to develop and validate a sepsis prediction model using structured electronic medical records (sEMR) and machine learning (ML) methods in emergency triage. The goal was to enhance early sepsis screening by integrating comprehensive tr...
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