AIMC Topic: Predictive Value of Tests

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Assessment of AI-based computational H&E staining versus chemical H&E staining for primary diagnosis in lymphomas: a brief interim report.

Journal of clinical pathology Feb 18, 2025
Microscopic review of tissue sections is of foundational importance in pathology, yet the traditional chemistry-based histology laboratory methods are labour intensive, tissue destructive, poorly scalable to the evolving needs of precision medicine a...
Humans Artificial Intelligence Machine Learning Staining and Labeling Eosine Yellowish-(YS) Hematoxylin Lymph Nodes Lymphoma Diagnosis, Differential Predictive Value of Tests Biopsy
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Stress hyperglycemia ratio and machine learning model for prediction of all-cause mortality in patients undergoing cardiac surgery.

Cardiovascular diabetology Feb 15, 2025
BACKGROUND: The stress hyperglycemia ratio (SHR) was developed to reduce the effects of long-term chronic glycemic factors on stress hyperglycemia levels, which was associated with adverse clinical outcomes. This study aims to evaluate the relationsh...
Middle Aged Risk Assessment Blood Glucose Stress, Physiological Time Factors Hospital Mortality Hyperglycemia Treatment Outcome Predictive Value of Tests Cause of Death Cardiac Surgical Procedures Retrospective Studies Female Risk Factors Humans Aged Machine Learning Male Decision Support Techniques Databases, Factual Biomarkers
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Predicting major adverse cardiac events in diabetes and chronic kidney disease: a machine learning study from the Silesia Diabetes-Heart Project.

Cardiovascular diabetology Feb 15, 2025
BACKGROUND: People living with diabetes mellitus (DM) and chronic kidney disease (CKD) are at significantly high risk of cardiovascular events (CVEs), however the predictive performance of traditional risk prediction methods are limited.
Prognosis Female Aged Male Predictive Value of Tests Middle Aged Risk Assessment Humans Heart Disease Risk Factors Incidence Machine Learning Time Factors Decision Support Techniques Renal Insufficiency, Chronic Databases, Factual Diabetes Mellitus
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Preoperatively predicting failure to achieve the minimum clinically important difference and the substantial clinical benefit in patient-reported outcome measures for total hip arthroplasty patients using machine learning.

BMC musculoskeletal disorders Feb 14, 2025
BACKGROUND: Attention to the collection of patient-reported outcomes measures (PROMs) associated with total hip arthroplasty (THA) is growing. The aim of this study was to preoperatively predict failure to achieve the minimal clinically important dif...
Patient Reported Outcome Measures Treatment Failure Arthroplasty, Replacement, Hip Osteoarthritis, Hip Minimal Clinically Important Difference Female Aged Humans Male Machine Learning Middle Aged Retrospective Studies Predictive Value of Tests
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Deep learning on pre-procedural computed tomography and clinical data predicts outcome following stroke thrombectomy.

Journal of neurointerventional surgery Feb 14, 2025
BACKGROUND: Deep learning using clinical and imaging data may improve pre-treatment prognostication in ischemic stroke patients undergoing endovascular thrombectomy (EVT).
Ischemic Stroke Tomography, X-Ray Computed Stroke Female Endovascular Procedures Aged Thrombectomy Aged, 80 and over Humans Middle Aged Computed Tomography Angiography Male Prognosis Deep Learning Retrospective Studies Cohort Studies Treatment Outcome Predictive Value of Tests
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Functional MRI-based machine learning strategy for prediction of postoperative delirium in cardiac surgery patients: A secondary analysis of a prospective observational study.

Journal of clinical anesthesia Feb 13, 2025
STUDY OBJECTIVE: Delirium is a common complication after cardiac surgery and is associated with poor prognosis. An effective delirium prediction model could identify high-risk patients who might benefit from targeted prevention strategies. We introdu...
Humans Machine Learning Prospective Studies Brain Female Aged Male Middle Aged Postoperative Complications China Emergence Delirium Magnetic Resonance Imaging Predictive Value of Tests Cardiac Surgical Procedures Delirium Heart Valve Prosthesis Implantation
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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...
Predictive Value of Tests Venous Thromboembolism Female Male Risk Factors Middle Aged Humans Hospitalization Risk Assessment Machine Learning Aged China Thoracic Injuries Adult ROC Curve Retrospective Studies
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Use of machine learning algorithms to construct models of symptom burden cluster risk in breast cancer patients undergoing chemotherapy.

Supportive care in cancer : official journal of the Multinational Association of Supportive Care in Cancer Feb 13, 2025
PURPOSE: To develop models using different machine learning algorithms to predict high-risk symptom burden clusters in breast cancer patients undergoing chemotherapy, and to determine an optimal model.
Breast Neoplasms Middle Aged Antineoplastic Agents ROC Curve Logistic Models Support Vector Machine Decision Trees Predictive Value of Tests Symptom Burden Algorithms Female Aged Adult Sensitivity and Specificity Humans Neural Networks, Computer Machine Learning
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Machine Learning Methods Based on Chest CT for Predicting the Risk of COVID-19-Associated Pulmonary Aspergillosis.

Academic radiology Feb 10, 2025
RATIONALE AND OBJECTIVES: To develop and validate a machine learning model based on chest CT and clinical risk factors to predict secondary aspergillus infection in hospitalized COVID-19 patients.
Humans Machine Learning COVID-19 Retrospective Studies Tomography, X-Ray Computed Male Middle Aged Risk Assessment Predictive Value of Tests Pulmonary Aspergillosis Female SARS-CoV-2 Risk Factors Aged Adult
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Comparing Phenotypes for Acute and Long-Term Response to Atrial Fibrillation Ablation Using Machine Learning.

Circulation. Arrhythmia and electrophysiology Feb 10, 2025
BACKGROUND: It is difficult to identify patients with atrial fibrillation (AF) most likely to respond to ablation. While any arrhythmia patient may recur after acutely successful ablation, AF is unusual in that patients may have long-term arrhythmia ...
Middle Aged Time Factors Electrocardiography Treatment Outcome Action Potentials Machine Learning Phenotype Female Aged Male Electrophysiologic Techniques, Cardiac Predictive Value of Tests Registries Atrial Fibrillation Heart Rate Catheter Ablation Humans
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