AIMC Topic: Aged, 80 and over
Clear Filters Showing 761 to 770 of 3432 articles
Establishment and validation of an artificial intelligence web application for predicting postoperative in-hospital mortality in patients with hip fracture: a national cohort study of 52 707 cases.
International journal of surgery (London, England)
Aug 1, 2024
BACKGROUND: In-hospital mortality following hip fractures is a significant concern, and accurate prediction of this outcome is crucial for appropriate clinical management. Nonetheless, there is a lack of effective prediction tools in clinical practic...
Evaluating performance of a near-infrared-spectroscopy-based and machine-learning-powered bio-optical sensitivity parameters in identifying intracranial hemorrhages in TBI across different age-groups.
Brain injury
Jul 31, 2024
PRIMARY OBJECTIVE: To evaluate the accuracy of an innovative machine-learning-powered near-infrared spectroscopy (mNIRS)-based bio-optical sensitivity parameters, namely specific tissue optical index (STOI) and intracranial tissue optical index (ITOI...
Age Factors
Adult
Brain Injuries, Traumatic
Sensitivity and Specificity
Double-Blind Method
Cross-Sectional Studies
Intracranial Hemorrhages
Male
Female
Adolescent
Middle Aged
Humans
Aged
Child, Preschool
Spectroscopy, Near-Infrared
Machine Learning
Aged, 80 and over
Child
Young Adult
Prospective Studies
A machine learning-based predictive model for the in-hospital mortality of critically ill patients with atrial fibrillation.
International journal of medical informatics
Jul 31, 2024
BACKGROUND: Atrial fibrillation (AF) is common among intensive care unit (ICU) patients and significantly raises the in-hospital mortality rate. Existing scoring systems or models have limited predictive capabilities for AF patients in ICU. Our study...
Comparison of machine learning and conventional statistical modeling for predicting readmission following acute heart failure hospitalization.
American heart journal
Jul 31, 2024
INTRODUCTION: Developing accurate models for predicting the risk of 30-day readmission is a major healthcare interest. Evidence suggests that models developed using machine learning (ML) may have better discrimination than conventional statistical mo...
Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET radiomics.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
Jul 31, 2024
PURPOSE: This study investigated imaging biomarkers derived from PSMA-PET acquired pre- and post-metastasis-directed therapy (MDT) to predict 2-year metastasis-free survival (MFS), which provides valuable early response assessment to improve patient ...
The potential of an artificial intelligence for diagnosing MRI images in rectal cancer: multicenter collaborative trial.
Journal of gastroenterology
Jul 31, 2024
BACKGROUND: An artificial intelligence-based algorithm we developed, mrAI, satisfactorily segmented the rectal tumor, rectum, and mesorectum from MRI data of rectal cancer patients in an initial study. Herein, we aimed to validate mrAI using an indep...
Predicting the severity of mood and neuropsychiatric symptoms from digital biomarkers using wearable physiological data and deep learning.
Computers in biology and medicine
Jul 31, 2024
Neuropsychiatric symptoms (NPS) and mood disorders are common in individuals with mild cognitive impairment (MCI) and increase the risk of progression to dementia. Wearable devices collecting physiological and behavioral data can help in remote, pass...
Clinical feasibility of deep learning based synthetic contrast enhanced abdominal CT in patients undergoing non enhanced CT scans.
Scientific reports
Jul 31, 2024
Our objective was to develop and evaluate the clinical feasibility of deep-learning-based synthetic contrast-enhanced computed tomography (DL-SynCCT) in patients designated for nonenhanced CT (NECT). We proposed a weakly supervised learning with the ...
SHAP based predictive modeling for 1 year all-cause readmission risk in elderly heart failure patients: feature selection and model interpretation.
Scientific reports
Jul 31, 2024
Heart failure (HF) is a significant global public health concern with a high readmission rate, posing a serious threat to the health of the elderly population. While several studies have used machine learning (ML) to develop all-cause readmission ris...
Deep learning identifies histopathologic changes in bladder cancers associated with smoke exposure status.
PloS one
Jul 31, 2024
Smoke exposure is associated with bladder cancer (BC). However, little is known about whether the histologic changes of BC can predict the status of smoke exposure. Given this knowledge gap, the current study investigated the potential association be...
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