AIMC Topic: Machine Learning

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On kernel machine learning for propensity score estimation under complex confounding structures.

Pharmaceutical statistics
Post marketing data offer rich information and cost-effective resources for physicians and policy-makers to address some critical scientific questions in clinical practice. However, the complex confounding structures (e.g., nonlinear and nonadditive ...

Super-resolution head and neck MRA using deep machine learning.

Magnetic resonance in medicine
PURPOSE: To probe the feasibility of deep learning-based super-resolution (SR) reconstruction applied to nonenhanced MR angiography (MRA) of the head and neck.

Establishment of noninvasive diabetes risk prediction model based on tongue features and machine learning techniques.

International journal of medical informatics
BACKGROUND: Diabetes is a chronic noncommunicable disease with high incidence rate. Diabetics without early diagnosis or standard treatment may contribute to serious multisystem complications, which can be life threatening. Timely detection and inter...

Personalized Risk Prediction for 30-Day Readmissions With Venous Thromboembolism Using Machine Learning.

Journal of nursing scholarship : an official publication of Sigma Theta Tau International Honor Society of Nursing
PURPOSE: The aim of the study was to develop and validate machine learning models to predict the personalized risk for 30-day readmission with venous thromboembolism (VTE).

How good is artificial intelligence (AI) at solving hairy problems? A review of AI applications in hair restoration and hair disorders.

Dermatologic therapy
Artificial intelligence (AI) applications in medicine are rapidly evolving. Deep learning diagnostic models that can accurately classify skin lesions have been developed. New AI applications are also starting to emerge in the hair restoration field. ...

A Review on Human-AI Interaction in Machine Learning and Insights for Medical Applications.

International journal of environmental research and public health
OBJECTIVE: To provide a human-Artificial Intelligence (AI) interaction review for Machine Learning (ML) applications to inform how to best combine both human domain expertise and computational power of ML methods. The review focuses on the medical fi...

A Machine Learning Processing Pipeline for Reliable Hand Gesture Classification of FMG Signals with Stochastic Variance.

Sensors (Basel, Switzerland)
ForceMyography (FMG) is an emerging competitor to surface ElectroMyography (sEMG) for hand gesture recognition. Most of the state-of-the-art research in this area explores different machine learning algorithms or feature engineering to improve hand g...

Assessment of acute kidney injury risk using a machine-learning guided generalized structural equation model: a cohort study.

BMC nephrology
BACKGROUND: Acute kidney injury is common in the surgical intensive care unit (ICU). It is associated with poor patient outcomes and high healthcare resource usage. This study's primary objective is to help identify which ICU patients are at high ris...

Learning From Past Respiratory Infections to Predict COVID-19 Outcomes: Retrospective Study.

Journal of medical Internet research
BACKGROUND: For the clinical care of patients with well-established diseases, randomized trials, literature, and research are supplemented with clinical judgment to understand disease prognosis and inform treatment choices. In the void created by a l...

Establishing Classifiers With Clinical Laboratory Indicators to Distinguish COVID-19 From Community-Acquired Pneumonia: Retrospective Cohort Study.

Journal of medical Internet research
BACKGROUND: The initial symptoms of patients with COVID-19 are very much like those of patients with community-acquired pneumonia (CAP); it is difficult to distinguish COVID-19 from CAP with clinical symptoms and imaging examination.