AIMC Topic: Predictive Value of Tests

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Prediction of stroke-associated hospital-acquired pneumonia: Machine learning approach.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Dec 12, 2024
BACKGROUND: Stroke-associated Hospital Acquired Pneumonia (HAP) significantly impacts patient outcomes. This study explores the utility of machine learning models in predicting HAP in stroke patients, leveraging national registry data and SHapley Add...
Male Middle Aged Risk Assessment Predictive Value of Tests Ischemic Stroke Female Risk Factors Aged Aged, 80 and over Humans Machine Learning Registries Decision Support Techniques Stroke Prognosis Hemorrhagic Stroke Healthcare-Associated Pneumonia
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Smartphone pupillometry with machine learning differentiates ischemic from hemorrhagic stroke: A pilot study.

Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association Dec 12, 2024
OBJECTIVES: Similarities between acute ischemic and hemorrhagic stroke make diagnosis and triage challenging. We studied a smartphone-based quantitative pupillometer for differentiation of acute ischemic and hemorrhagic stroke.
Diagnostic Techniques, Ophthalmological Hemorrhagic Stroke Reflex, Pupillary Smartphone Diagnosis, Differential Predictive Value of Tests Ischemic Stroke Pupil Male Middle Aged Case-Control Studies Pilot Projects Machine Learning Humans Female Reproducibility of Results Aged Aged, 80 and over
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Artificial Intelligence-Driven Assessment of Coronary Computed Tomography Angiography for Intermediate Stenosis: Comparison With Quantitative Coronary Angiography and Fractional Flow Reserve.

The American journal of cardiology Dec 11, 2024
We aimed to compare artificial intelligence (AI)-based coronary stenosis evaluation of coronary computed tomography angiography (CCTA) with its quantitative counterpart of invasive coronary angiography (ICA) and invasive fractional flow reserve (FFR)...
Humans Artificial Intelligence Retrospective Studies Female Aged Male Middle Aged Computed Tomography Angiography Coronary Stenosis Fractional Flow Reserve, Myocardial Severity of Illness Index Predictive Value of Tests Coronary Angiography Coronary Vessels
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Machine Learning-Based Prediction for In-Hospital Mortality After Acute Intracerebral Hemorrhage Using Real-World Clinical and Image Data.

Journal of the American Heart Association Dec 10, 2024
BACKGROUND: Machine learning (ML) techniques are widely employed across various domains to achieve accurate predictions. This study assessed the effectiveness of ML in predicting early mortality risk among patients with acute intracerebral hemorrhage...
Risk Factors Aged Aged, 80 and over Male Middle Aged Prognosis Retrospective Studies Tomography, X-Ray Computed Female Acute Disease Risk Assessment Hospital Mortality ROC Curve Cerebral Hemorrhage Predictive Value of Tests Humans Machine Learning
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Utility of a Large Language Model for Extraction of Clinical Findings from Healthcare Data following Lung Ablation: A Feasibility Study.

Journal of vascular and interventional radiology : JVIR Dec 9, 2024
To assess the feasibility of utilizing a large language model (LLM) in extracting clinically relevant information from healthcare data in patients who have undergone microwave ablation for lung tumors. In this single-center retrospective study, radio...
Microwaves Recurrence Large Language Models Hemothorax Ablation Techniques Pneumothorax Hemoptysis Predictive Value of Tests Natural Language Processing Feasibility Studies Neoplasm Recurrence, Local Data Mining Middle Aged Time Factors Humans Lung Neoplasms Retrospective Studies Treatment Outcome Electronic Health Records Female Reproducibility of Results Aged Male
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Prediction of Two Year Survival Following Elective Repair of Abdominal Aortic Aneurysms at A Single Centre Using A Random Forest Classification Algorithm.

European journal of vascular and endovascular surgery : the official journal of the European Society for Vascular Surgery Dec 9, 2024
OBJECTIVE: The decision to electively repair an abdominal aortic aneurysm (AAA) involves balancing the risk of rupture, peri-procedural death, and life expectancy. Random forest classifiers (RFCs) are powerful machine learning algorithms. The aim of ...
Aortic Aneurysm, Abdominal Elective Surgical Procedures Random Forest Endovascular Procedures Risk Assessment Time Factors Treatment Outcome Predictive Value of Tests Humans Machine Learning Algorithms Retrospective Studies Female Risk Factors Aged Aged, 80 and over Male Middle Aged
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Impact of Inflammation After Cardiac Surgery on 30-Day Mortality and Machine Learning Risk Prediction.

Journal of cardiothoracic and vascular anesthesia Dec 9, 2024
OBJECTIVES: To investigate the impact of systemic inflammatory response syndrome (SIRS) on 30-day mortality following cardiac surgery and develop a machine learning model to predict SIRS.
Risk Factors Postoperative Complications Cardiac Surgical Procedures Aged Cohort Studies Male Systemic Inflammatory Response Syndrome Humans Middle Aged Predictive Value of Tests Risk Assessment Machine Learning Retrospective Studies Female
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Advancing personalised care in atrial fibrillation and stroke: The potential impact of AI from prevention to rehabilitation.

Trends in cardiovascular medicine Dec 7, 2024
Atrial fibrillation (AF) is a complex condition caused by various underlying pathophysiological disorders and is the most common heart arrhythmia worldwide, affecting 2 % of the European population. This prevalence increases with age, imposing signif...
Humans Artificial Intelligence Machine Learning Decision Support Techniques Recovery of Function Precision Medicine Predictive Value of Tests Risk Factors Risk Assessment Stroke Treatment Outcome Atrial Fibrillation Stroke Rehabilitation
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Prediction of Symptomatic Intracranial Hemorrhage Before Mechanical Thrombectomy Using Machine Learning in Patients with Anterior Circulation Large Vessel Occlusion.

World neurosurgery Dec 6, 2024
BACKGROUND: Symptomatic intracranial hemorrhage (sICH) after mechanical thrombectomy (MT) is associated with worse outcomes. We sought to develop and internally validate a machine learning (ML) model to predict sICH prior to MT in patients with anter...
Humans Male Middle Aged Predictive Value of Tests Intracranial Hemorrhages Machine Learning Thrombectomy Retrospective Studies Female Aged Aged, 80 and over
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Machine learning for predicting poor outcomes in aneurysmal subarachnoid hemorrhage: A systematic review and meta-analysis involving 8445 participants.

Clinical neurology and neurosurgery Dec 5, 2024
Early prediction of poor outcomes in patients impacted with aneurysmal subarachnoid hemorrhage (aSAH) is crucial for timely intervention and effective management. This systematic review and meta-analysis aimed to evaluate the performance of machine l...
Prognosis Predictive Value of Tests Subarachnoid Hemorrhage Humans Machine Learning
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