AIMC Topic: Risk

Clear Filters Showing 61 to 70 of 145 articles

Multicategory individualized treatment regime using outcome weighted learning.

Biometrics
Individualized treatment regimes (ITRs) aim to recommend treatments based on patient-specific characteristics in order to maximize the expected clinical outcome. Outcome weighted learning approaches have been proposed for this optimization problem wi...

Neural networks versus Logistic regression for 30 days all-cause readmission prediction.

Scientific reports
Heart failure (HF) is one of the leading causes of hospital admissions in the US. Readmission within 30 days after a HF hospitalization is both a recognized indicator for disease progression and a source of considerable financial burden to the health...

Artificial Intelligence and Arthroplasty at a Single Institution: Real-World Applications of Machine Learning to Big Data, Value-Based Care, Mobile Health, and Remote Patient Monitoring.

The Journal of arthroplasty
BACKGROUND: Driven by the recent ubiquity of big data and computing power, we established the Machine Learning Arthroplasty Laboratory (MLAL) to examine and apply artificial intelligence (AI) to musculoskeletal medicine.

A machine learning-based approach for predicting the outbreak of cardiovascular diseases in patients on dialysis.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with End- Stage Kidney Disease (ESKD) have a unique cardiovascular risk. This study aims at predicting, with a certain precision, death and cardiovascular diseases in dialysis patients.

Statistical characterization and classification of colon microarray gene expression data using multiple machine learning paradigms.

Computer methods and programs in biomedicine
OBJECTIVE: A colon microarray data is a repository of thousands of gene expressions with different strengths for each cancer cell. It is necessary to detect which genes are responsible for cancer growth. This study presents an exhaustive comparative ...

A convolutional neural network algorithm for automatic segmentation of head and neck organs at risk using deep lifelong learning.

Medical physics
PURPOSE: This study suggests a lifelong learning-based convolutional neural network (LL-CNN) algorithm as a superior alternative to single-task learning approaches for automatic segmentation of head and neck (OARs) organs at risk.

Enhancing the Ambient Assisted Living Capabilities with a Mobile Robot.

Computational intelligence and neuroscience
Ambient assisted living (AAL) environments are currently a key focus of interest as an option to assist and monitor disabled and elderly people. These systems can improve their quality of life and personal autonomy by detecting events such as enterin...

Semi-supervised learning to improve generalizability of risk prediction models.

Journal of biomedical informatics
The utility of a prediction model depends on its generalizability to patients drawn from different but related populations. We explored whether a semi-supervised learning model could improve the generalizability of colorectal cancer (CRC) risk predic...