AI Medical Compendium Topic:
Models, Statistical

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Discovering hidden knowledge through auditing clinical diagnostic knowledge bases.

Journal of biomedical informatics
OBJECTIVE: Evaluate potential for data mining auditing techniques to identify hidden concepts in diagnostic knowledge bases (KB). Improving completeness enhances KB applications such as differential diagnosis and patient case simulation.

Prediction task guided representation learning of medical codes in EHR.

Journal of biomedical informatics
There have been rapidly growing applications using machine learning models for predictive analytics in Electronic Health Records (EHR) to improve the quality of hospital services and the efficiency of healthcare resource utilization. A fundamental an...

Rough sets and Laplacian score based cost-sensitive feature selection.

PloS one
Cost-sensitive feature selection learning is an important preprocessing step in machine learning and data mining. Recently, most existing cost-sensitive feature selection algorithms are heuristic algorithms, which evaluate the importance of each feat...

Marginal Structural Models with Counterfactual Effect Modifiers.

The international journal of biostatistics
UNLABELLED: In health and social sciences, research questions often involve systematic assessment of the modification of treatment causal effect by patient characteristics. In longitudinal settings, time-varying or post-intervention effect modifiers ...

Unsupervised Bayesian Inference to Fuse Biosignal Sensory Estimates for Personalizing Care.

IEEE journal of biomedical and health informatics
The role of sensing technologies, such as wearables, in delivering precision care is becoming widely acceptable. Given the very large quantities of sensor data that rapidly accumulate, there is a need to employ automated algorithms to label biosignal...

Augmented outcome-weighted learning for estimating optimal dynamic treatment regimens.

Statistics in medicine
Dynamic treatment regimens (DTRs) are sequential treatment decisions tailored by patient's evolving features and intermediate outcomes at each treatment stage. Patient heterogeneity and the complexity and chronicity of many diseases call for learning...

Standard errors and confidence intervals for variable importance in random forest regression, classification, and survival.

Statistics in medicine
Random forests are a popular nonparametric tree ensemble procedure with broad applications to data analysis. While its widespread popularity stems from its prediction performance, an equally important feature is that it provides a fully nonparametric...

Estimating Brain Connectivity With Varying-Length Time Lags Using a Recurrent Neural Network.

IEEE transactions on bio-medical engineering
OBJECTIVE: Computer-aided estimation of brain connectivity aims to reveal information propagation in brain automatically, which has great potential in clinical applications, e.g., epilepsy foci diagnosis. Granger causality is an effective tool for di...