AIMC Topic: Retrospective Studies
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Large Language Models as Decision-Making Tools in Oncology: Comparing Artificial Intelligence Suggestions and Expert Recommendations.
JCO clinical cancer informatics
Mar 20, 2025
PURPOSE: To determine the accuracy of large language models (LLMs) in generating appropriate treatment options for patients with early breast cancer on the basis of their medical records.
Enhanced machine learning predictive modeling for delirium in elderly ICU patients with COPD and respiratory failure: A retrospective study based on MIMIC-IV.
PloS one
Mar 20, 2025
BACKGROUND AND OBJECTIVE: Elderly patients with Chronic obstructive pulmonary disease (COPD) and respiratory failure admitted to the intensive care unit (ICU) have a poor prognosis, and the occurrence of delirium further worsens outcomes and increase...
Identifying liver cirrhosis in patients with chronic hepatitis B: an interpretable machine learning algorithm based on LSM.
Annals of medicine
Mar 19, 2025
BACKGROUND: Chronic hepatitis B (CHB) is a common cause of liver cirrhosis (LC), a condition associated with an unfavourable prognosis. Therefore, timely diagnosis of LC in CHB patients is crucial.
Machine learning models for enhanced diagnosis and risk assessment of prostate cancer with Ga-PSMA-617 PET/CT.
European journal of radiology
Mar 19, 2025
OBJECTIVE: Prostate cancer (PCa) is highly heterogeneous, making early detection of adverse pathological features crucial for improving patient outcomes. This study aims to predict PCa aggressiveness and identify radiomic and protein biomarkers assoc...
Male
Middle Aged
Risk Assessment
Positron Emission Tomography Computed Tomography
Aged
Aged, 80 and over
Sensitivity and Specificity
Biomarkers, Tumor
Gallium Radioisotopes
Gallium Isotopes
Heterocyclic Compounds, 1-Ring
Prostatic Neoplasms
Prostate-Specific Antigen
Humans
Radiopharmaceuticals
Machine Learning
Dipeptides
Retrospective Studies
Closing the gap in plan quality: Leveraging deep-learning dose prediction for adaptive radiotherapy.
Journal of applied clinical medical physics
Mar 19, 2025
PURPOSE: Balancing quality and efficiency has been a challenge for online adaptive therapy. Most systems start the online re-optimization with the original planning goals. While some systems allow planners to modify the planning goals, achieving a hi...
Predicting postoperative pulmonary infection in elderly patients undergoing major surgery: a study based on logistic regression and machine learning models.
BMC pulmonary medicine
Mar 19, 2025
BACKGROUND: Postoperative pulmonary infection (POI) is strongly associated with a poor prognosis and has a high incidence in elderly patients undergoing major surgery. Machine learning (ML) algorithms are increasingly being used in medicine, but the ...
Comparison of machine learning and nomogram to predict 30-day in-hospital mortality in patients with acute myocardial infarction combined with cardiogenic shock: a retrospective study based on the eICU-CRD and MIMIC-IV databases.
BMC cardiovascular disorders
Mar 19, 2025
BACKGROUND: To evaluate the predictive utility of machine learning and nomogram in predicting in-hospital mortality in patients with acute myocardial infarction complicated by cardiogenic shock (AMI-CS), and to visualize the model results in order to...
Machine learning prediction model for functional prognosis of acute ischemic stroke based on MRI radiomics of white matter hyperintensities.
BMC medical imaging
Mar 19, 2025
OBJECTIVE: The purpose of the current study is to explore the value of a nomogram that integrates clinical factors and MRI white matter hyperintensities (WMH) radiomics features in predicting the prognosis at 90 days for patients with acute ischemic ...
Predictive modeling of pregnancy outcomes utilizing multiple machine learning techniques for in vitro fertilization-embryo transfer.
BMC pregnancy and childbirth
Mar 19, 2025
OBJECTIVE: This study aims to investigate the influencing factors of pregnancy outcomes during in vitro fertilization and embryo transfer (IVF-ET) procedures in clinical practice. Several prediction models were constructed to predict pregnancy outcom...
Predicting intra-abdominal hypertension using anthropometric measurements and machine learning.
Scientific reports
Mar 19, 2025
Almost one in four critically ill patients suffer from intra-abdominal hypertension (IAH). Currently, the gold standard for measuring intra-abdominal pressure (IAP) is via the bladder. Measurement of IAP is important to identify IAH early and thus im...
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