AI Medical Compendium Topic:
Models, Statistical

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Using machine learning and an ensemble of methods to predict kidney transplant survival.

PloS one
We used an ensemble of statistical methods to build a model that predicts kidney transplant survival and identifies important predictive variables. The proposed model achieved better performance, measured by Harrell's concordance index, than the Esti...

Blind Noisy Image Quality Assessment Using Sub-Band Kurtosis.

IEEE transactions on cybernetics
Noise that afflicts natural images, regardless of the source, generally disturbs the perception of image quality by introducing a high-frequency random element that, when severe, can mask image content. Except at very low levels, where it may play a ...

An Ensemble Model With Clustering Assumption for Warfarin Dose Prediction in Chinese Patients.

IEEE journal of biomedical and health informatics
The prediction of daily stable warfarin dosage for a specific patient is difficult. To improve the predictive accuracy and to build a highly accurate predictive model, we developed an ensemble learning method, called evolutionary fuzzy c-mean (EFCM) ...

Automatically Evaluating Balance: A Machine Learning Approach.

IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
Compared to in-clinic balance training, in-home training is not as effective. This is, in part, due to the lack of feedback from physical therapists (PTs). In this paper, we analyze the feasibility of using trunk sway data and machine learning (ML) t...

Discovering functional impacts of miRNAs in cancers using a causal deep learning model.

BMC medical genomics
BACKGROUND: Micro-RNAs (miRNAs) play a significant role in regulating gene expression under physiological and pathological conditions such as cancers. However, it remains a challenging problem to discover the target messenger RNAs (mRNAs) of a miRNA ...

Primary Sclerosing Cholangitis Risk Estimate Tool (PREsTo) Predicts Outcomes of the Disease: A Derivation and Validation Study Using Machine Learning.

Hepatology (Baltimore, Md.)
Improved methods are needed to risk stratify and predict outcomes in patients with primary sclerosing cholangitis (PSC). Therefore, we sought to derive and validate a prediction model and compare its performance to existing surrogate markers. The mod...

A novel prediction method for lymph node involvement in endometrial cancer: machine learning.

International journal of gynecological cancer : official journal of the International Gynecological Cancer Society
OBJECTIVE: The necessity of lymphadenectomy and the prediction of lymph node involvement (LNI) in endometrial cancer (EC) have been hotly-debated questions in recent years. Machine learning is a broad field that can produce results and estimations. I...

Stability Analysis for Delayed Neural Networks via Improved Auxiliary Polynomial-Based Functions.

IEEE transactions on neural networks and learning systems
This brief is concerned with stability analysis for delayed neural networks (DNNs). By establishing polynomials and introducing slack variables reasonably, some improved delay-product type of auxiliary polynomial-based functions (APFs) is developed t...

Probabilistic Kinematic Model of a Robotic Catheter for 3D Position Control.

Soft robotics
Continuum robots offer compliant and dexterous operations, which are suitable to be used in unstructured environments. Tendon-driven catheters, owing to their continuum structure, are applied in minimal invasive surgeries such as intracardiac interve...

Spatiotemporal continuous estimates of PM concentrations in China, 2000-2016: A machine learning method with inputs from satellites, chemical transport model, and ground observations.

Environment international
Ambient exposure to fine particulate matter (PM) is known to harm public health in China. Satellite remote sensing measurements of aerosol optical depth (AOD) were statistically associated with in-situ observations after 2013 to predict PM concentrat...