AIMC Topic: Aged
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Machine Learning Based Prediction of Imminent ICP Insults During Neurocritical Care of Traumatic Brain Injury.
Neurocritical care
Sep 25, 2024
BACKGROUND: In neurointensive care, increased intracranial pressure (ICP) is a feared secondary brain insult in traumatic brain injury (TBI). A system that predicts ICP insults before they emerge may facilitate early optimization of the physiology, w...
Accuracy assessment of artificial intelligence IOL calculation formulae: utilizing the heteroscedastic statistics and the Eyetemis Analysis Tool.
Eye (London, England)
Sep 25, 2024
OBJECTIVES: To analyse the accuracy of artificial intelligence (AI)-driven intraocular (IOL) calculation formulae, together with established formulae using the heteroscedastic methodology and the Eyetemis Analysis Tool.
Bowel preparation before elective right colectomy: Multitreatment machine-learning analysis on 2,617 patients.
Surgery
Sep 25, 2024
BACKGROUND: In the worldwide, real-life setting, some candidates for right colectomy still receive no bowel preparation, some receive oral antibiotics alone, some receive mechanical bowel preparation alone, and some receive mechanical bowel preparati...
Integrating machine learning and multi-omics analysis to develop an asparagine metabolism immunity index for improving clinical outcome and drug sensitivity in lung adenocarcinoma.
Immunologic research
Sep 25, 2024
Lung adenocarcinoma (LUAD) is a malignancy affecting the respiratory system. Most patients are diagnosed with advanced or metastatic lung cancer due to the fact that most of their clinical symptoms are insidious, resulting in a bleak prognosis. Given...
Prediction model of in-hospital cardiac arrest using machine learning in the early phase of hospitalization.
The Kaohsiung journal of medical sciences
Sep 25, 2024
In hospitals, the deterioration of a patient's condition leading to death is often preceded by physiological abnormalities in the hours to days beforehand. Several risk-scoring systems have been developed to identify patients at risk of major adverse...
Radiomics prediction models of left atrial appendage hypercoagulability based on machine learning algorithms: an exploration about cardiac computed tomography angiography imaging.
The international journal of cardiovascular imaging
Sep 25, 2024
Transesophageal echocardiography (TEE) is the standard method for diagnosing left atrial appendage (LAA) hypercoagulability in patients with atrial fibrillation (AF), which means LAA thrombus/sludge, dense spontaneous echo contrast and slow LAA blood...
Radiomics
Male
Atrial Fibrillation
Female
Middle Aged
Predictive Value of Tests
Humans
Reproducibility of Results
Risk Assessment
Thrombophilia
Risk Factors
Machine Learning
Anticoagulants
Nomograms
Multidetector Computed Tomography
Aged
Decision Support Techniques
Computed Tomography Angiography
Blood Coagulation
Retrospective Studies
Atrial Appendage
Coronary Angiography
Radiographic Image Interpretation, Computer-Assisted
Development and validation of a machine learning model to predict myocardial blood flow and clinical outcomes from patients' electrocardiograms.
Cell reports. Medicine
Sep 25, 2024
We develop a machine learning (ML) model using electrocardiography (ECG) to predict myocardial blood flow reserve (MFR) and assess its prognostic value for major adverse cardiovascular events (MACEs). Using 3,639 ECG-positron emission tomography (PET...
Pretreatment CT-based machine learning radiomics model predicts response in unresectable hepatocellular carcinoma treated with lenvatinib plus PD-1 inhibitors and interventional therapy.
Journal for immunotherapy of cancer
Sep 25, 2024
[How well does artificial intelligence detect fractures in the cervical spine on CT?].
Nederlands tijdschrift voor geneeskunde
Sep 25, 2024
OBJECTIVE: To compare diagnostic accuracy of artificial intelligence (AI) for cervical spine (C-spine) fracture detection on CT with attending radiologists.
Deep learning for blood glucose level prediction: How well do models generalize across different data sets?
PloS one
Sep 25, 2024
Deep learning-based models for predicting blood glucose levels in diabetic patients can facilitate proactive measures to prevent critical events and are essential for closed-loop control therapy systems. However, selecting appropriate models from the...
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