AIMC Topic: Middle Aged

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Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.

Hepatology communications
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelli...

Prediction Model Using Machine Learning for Mortality in Patients with Heart Failure.

The American journal of cardiology
Heart Failure (HF) is a major cause of morbidity and mortality in the US. With aging of the US population, the public health burden of HF is enormous. We aimed to develop an ensemble prediction model for 30-day mortality after discharge using machine...

Robot-assisted beating-heart surgery for atrial septal defect repair in a case of situs inversus totalis with dextrocardia.

The international journal of medical robotics + computer assisted surgery : MRCAS
BACKGROUND: This paper describes a case of a patient with situs inversus totalis (SIT) and dextrocardia in which robotic atrial septal defect (ASD) repair was successfully performed in a beating heart.

Bone mineral density response prediction following osteoporosis treatment using machine learning to aid personalized therapy.

Scientific reports
Osteoporosis is a global health problem for ageing populations. The goals of osteoporosis treatment are to improve bone mineral density (BMD) and prevent fractures. One major obstacle that remains a great challenge to achieve the goals is how to sele...

Development and Validation of a Magnetic Resonance Imaging-Based Machine Learning Model for TMJ Pathologies.

BioMed research international
The purpose of this study was to propose a machine learning model and assess its ability to classify TMJ pathologies on magnetic resonance (MR) images. This retrospective cohort study included 214 TMJs from 107 patients with TMJ signs and symptoms. A...

Clinical Feature-Based Machine Learning Model for 1-Year Mortality Risk Prediction of ST-Segment Elevation Myocardial Infarction in Patients with Hyperuricemia: A Retrospective Study.

Computational and mathematical methods in medicine
Accurate risk assessment of high-risk patients is essential in clinical practice. However, there is no practical method to predict or monitor the prognosis of patients with ST-segment elevation myocardial infarction (STEMI) complicated by hyperuricem...

A fusion decision system to identify and grade malnutrition in cancer patients: Machine learning reveals feasible workflow from representative real-world data.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND AND AIMS: Most nutritional assessment tools are based on pre-defined questionnaires or consensus guidelines. However, it has been postulated that population data can be used directly to develop a solution for assessing malnutrition. This s...