AIMC Topic: Middle Aged

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Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.

International journal of medical informatics
BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for ischemic stroke patients in the intensive care unit (ICU). However, the interpretation and translation of predictive models into clinical applications ar...

Artificial intelligence driven plaque characterization and functional assessment from CCTA using OCT-based automation: A prospective study.

International journal of cardiology
BACKGROUND: We aimed to develop and validate an Artificial Intelligence (AI) model that leverages CCTA and optical coherence tomography (OCT) images for automated analysis of plaque characteristics and coronary function.

Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.

Computers in biology and medicine
OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model which employed to predict cardiovascular disease (CVD) risk scores from retinal fundus photos.

Dynamic HRV Monitoring and Machine Learning Predict NYHA Improvements in Acute Heart Failure Patients.

Computers in biology and medicine
Heart failure (HF) is marked by significant morbidity, mortality, and readmission rates, highlighting a critical need for accurate assessment of treatment efficacy. The New York Heart Association (NYHA) classification, while standard, falls short in ...

An explainable non-invasive hybrid machine learning framework for accurate prediction of thyroid-stimulating hormone levels.

Computers in biology and medicine
Machine learning models, including thyroid biomarkers, are increasingly utilized in healthcare for biomarker prediction. These models offer the potential to enhance disease diagnosis through data-driven approaches relying on non-invasive techniques. ...

Development and validation of radiomics and deep transfer learning models to assess cognitive impairment in patients with cerebral small vessel disease.

Neuroscience
Cognitive impairment in cerebral small vessel disease (CSVD) progresses subtly but carries significant clinical consequences, necessitating effective diagnostic tools. This study developed and validated predictive models for CSVD-related cognitive im...

Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.

Endocrine
BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having varying degrees of malignancy risk, with the risk increasing from TI-RADS 3 to TI-RADS 5. While some ...

Machine learning to detect Alzheimer's disease with data on drugs and diagnoses.

The journal of prevention of Alzheimer's disease
BACKGROUND: Integrating machine learning with medical records offers potential for early detection of Alzheimer's disease (AD), enabling timely interventions.

Machine Learning Models Integrating Dietary Indicators Improve the Prediction of Progression from Prediabetes to Type 2 Diabetes Mellitus.

Nutrients
: Diet plays an important role in preventing and managing the progression from prediabetes to type 2 diabetes mellitus (T2DM). This study aims to develop prediction models incorporating specific dietary indicators and explore the performance in T2DM ...