AIMC Topic: Retrospective Studies

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Convolutional neural networks for automatic MR classification of myocardial iron overload in thalassemia major patients.

European radiology Dec 10, 2024
OBJECTIVES: To develop a deep-learning model for supervised classification of myocardial iron overload (MIO) from magnitude T2* multi-echo MR images.
Humans Neural Networks, Computer Retrospective Studies Female Young Adult Adult Sensitivity and Specificity Male Deep Learning Convolutional Neural Networks Adolescent Child Magnetic Resonance Imaging beta-Thalassemia Image Interpretation, Computer-Assisted Iron Overload
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Utilizing deep learning-based causal inference to explore vancomycin's impact on continuous kidney replacement therapy necessity in blood culture-positive intensive care unit patients.

Microbiology spectrum Dec 10, 2024
Patients with positive blood cultures in the intensive care unit (ICU) are at high risk for septic acute kidney injury requiring continuous kidney replacement therapy (CKRT), especially when treated with vancomycin. This study developed a machine lea...
Middle Aged Deep Learning Sepsis Blood Culture Vancomycin Intensive Care Units Female Aged Adult Male Anti-Bacterial Agents Retrospective Studies Continuous Renal Replacement Therapy Acute Kidney Injury Humans Machine Learning
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End-to-end deep learning patient level classification of affected territory of ischemic stroke patients in DW-MRI.

Neuroradiology Dec 10, 2024
PURPOSE: To develop an end-to-end DL model for automated classification of affected territory in DWI of stroke patients.
Aged, 80 and over Adult Male Middle Aged Deep Learning Ischemic Stroke Image Interpretation, Computer-Assisted Diffusion Magnetic Resonance Imaging Humans Retrospective Studies Female Aged
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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...
Male Middle Aged Risk Assessment Hospital Mortality ROC Curve Female Risk Factors Aged Aged, 80 and over Cerebral Hemorrhage Predictive Value of Tests Acute Disease Humans Machine Learning Prognosis Retrospective Studies Tomography, X-Ray Computed
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Predicting Early Hospital Discharge Following Revision Total Hip Arthroplasty: An Analysis of a Large National Database Using Machine Learning.

The Journal of arthroplasty Dec 9, 2024
BACKGROUND: Revision total hip arthroplasty (rTHA) was recently removed from the Medicare inpatient-only list. However, appropriate candidate selection for outpatient rTHA remains paramount. The purpose of this study was to evaluate the utility of a ...
Humans Machine Learning Databases, Factual Retrospective Studies Logistic Models United States Patient Discharge Reoperation Arthroplasty, Replacement, Hip Female Aged Aged, 80 and over Male Middle Aged
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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...
Large Language Models Humans Hemothorax Ablation Techniques Retrospective Studies Pneumothorax Electronic Health Records Hemoptysis Female Lung Neoplasms Neoplasm Recurrence, Local Reproducibility of Results Data Mining Treatment Outcome Aged Microwaves Predictive Value of Tests Male Recurrence Natural Language Processing Middle Aged Feasibility Studies Time Factors
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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 ...
Endovascular Procedures Treatment Outcome Predictive Value of Tests Algorithms Aortic Aneurysm, Abdominal Retrospective Studies Elective Surgical Procedures Female Random Forest Risk Factors Aged Aged, 80 and over Male Middle Aged Risk Assessment Time Factors Humans Machine Learning
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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.
Postoperative Complications Cohort Studies Risk Factors Systemic Inflammatory Response Syndrome Humans Predictive Value of Tests Aged Machine Learning Cardiac Surgical Procedures Male Retrospective Studies Middle Aged Female Risk Assessment
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Prediction of Visual Acuity After Cataract Surgery by Deep Learning Methods Using Clinical Information and Color Fundus Photography.

Current eye research Dec 9, 2024
PURPOSE: To examine the performance of deep-learning models that predicts the visual acuity after cataract surgery using preoperative clinical information and color fundus photography (CFP).
Female Aged Aged, 80 and over Male Middle Aged Humans Retrospective Studies Fundus Oculi Postoperative Period Photography Deep Learning Visual Acuity Cataract Extraction Cataract
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Artificial intelligence aided serum protein electrophoresis analysis of Finnish patient samples: Retrospective validation.

Clinica chimica acta; international journal of clinical chemistry Dec 9, 2024
BACKGROUND AND AIMS: Serum protein electrophoresis interpretation requires a substantial amount of manual work. In 2020, Chabrun et al. created a machine learning method called SPECTR for the task. We aimed to validate and test the SPECTR method agai...
Blood Protein Electrophoresis Male Middle Aged Blood Proteins Humans Finland Neural Networks, Computer Artificial Intelligence Retrospective Studies Female
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