AIMC Topic: Middle Aged

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Machine Learning Models to Predict Bone Metastasis Risk in Patients With Lung Cancer.

Cancer medicine
INTRODUCTION: The aim of this study was to find the most appropriate variables to input into machine learning algorithms to identify those patients with primary lung malignancy with high risk for metastasis to the bone.

Can machine learning models improve the prediction of surgical site infection in abdominal surgery than traditional statistical models?

The Journal of international medical research
OBJECTIVE: To externally validate by revision and update the study on the efficacy of nosocomial infection control (SENIC) model of surgical site infection (SSI) using logistic regression (LR) and machine learning (ML) approaches.

Accelerating Brain MR Imaging With Multisequence and Convolutional Neural Networks.

Brain and behavior
PURPOSE: Magnetic resonance imaging (MRI) refers to one of the critical image modalities for diagnosis, whereas its long acquisition time limits its application. In this study, the aim was to investigate whether deep learning-based techniques are cap...

Proactive care management of AI-identified at-risk patients decreases preventable admissions.

The American journal of managed care
OBJECTIVES: We assessed whether proactive care management for artificial intelligence (AI)-identified at-risk patients reduced preventable emergency department (ED) visits and hospital admissions (HAs).

Early Detection of Left Ventricular Dysfunction With Machine Learning-Based Strain Imaging in Aortic Stenosis Patients.

Echocardiography (Mount Kisco, N.Y.)
PURPOSE: Aortic stenosis (AS) is a common cardiovascular condition where early detection of left ventricular (LV) dysfunction is essential for timely intervention and optimal management. Current echocardiographic measurements, such as ejection fracti...

Integrated machine learning developed a prognosis-related gene signature to predict prognosis in oesophageal squamous cell carcinoma.

Journal of cellular and molecular medicine
The mortality rate of oesophageal squamous cell carcinoma (ESCC) remains high, and conventional TNM systems cannot accurately predict its prognosis, thus necessitating a predictive model. In this study, a 17-gene prognosis-related gene signature (PRS...

Frequency-Specific Alternations in the Amplitude of Fluctuations in Tension-Type Headache: A Machine Learning Study.

Journal of neuroscience research
Brain neural signal at different frequency bands relates to different functions. However, the frequency-specific properties of spontaneous brain activity in tension-type headache (TTH)-the most rampant primary headache-remain largely unknown. We inve...

Predicting Pancreatic Cancer in New-Onset Diabetes Cohort Using a Novel Model With Integrated Clinical and Genetic Indicators: A Large-Scale Prospective Cohort Study.

Cancer medicine
INTRODUCTION: Individuals who develop new-onset diabetes have been identified as a high-risk cohort for pancreatic cancer (PC), exhibiting an incidence rate nearly 8 times higher than the general population. Hence, the targeted screening of this spec...

Comparing machine learning and deep learning models to predict cognition progression in Parkinson's disease.

Clinical and translational science
Cognitive decline in Parkinson's disease (PD) varies widely. While models to predict cognitive progression exist, comparing traditional probabilistic models to deep learning methods remains understudied. This study compares sequential modeling techni...

Using machine learning to construct the diagnosis model of female bladder outlet obstruction based on urodynamic study data.

Investigative and clinical urology
PURPOSE: To intelligently diagnose whether there is bladder outlet obstruction (BOO) in female with decent detrusor contraction ability by focusing on urodynamic study (UDS) data.