AIMC Topic: Machine Learning

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Is Regular Re-Training of a Predictive Delirium Model Necessary After Deployment in Routine Care?

Studies in health technology and informatics
Adoption of electronic medical records in hospitals generates a large amount of data. Health care professionals can easily lose their sight on the important insights of the patients' clinical and medical history. Although machine learning algorithms ...

Ensemble Based Approach for Time Series Classification in Metabolomics.

Studies in health technology and informatics
BACKGROUND: Machine learning is one important application in the area of health informatics, however classification methods for longitudinal data are still rare.

Information Adapted Machine Learning Models for Prediction in Clinical Workflow.

Studies in health technology and informatics
BACKGROUND: In a database of electronic health records, the amount of available information varies widely between patients. In a real-time prediction scenario, a machine learning model may receive limited information for some patients.

Evaluation of Depth Cameras for Use as an Augmented Reality Emergency Ruler.

Studies in health technology and informatics
Children are rarely affected by medical emergencies. The experience of doctors or paramedics with child emergencies is correspondingly poor. The anatomical features and individual calculations make such an emergency much more error-prone than a compa...

Artificial intelligence and modern information and communication technologies entering medicine.

Casopis lekaru ceskych
Many new technologies based on computer technologies which are very successful in industry spread over the medicine and became integral part of all its disciplines. Artificial intelligence opened new possibilities for managing and solving many proble...

Perceptual Effects of Adjusting Hearing-Aid Gain by Means of a Machine-Learning Approach Based on Individual User Preference.

Trends in hearing
This study investigated a method to adjust hearing-aid gain by use of a machine-learning algorithm that estimates the optimal setting of gain parameters based on user preference indicated in an iterative paired-comparison procedure. Twenty hearing-im...

Personalized Pancreatic Cancer Management: A Systematic Review of How Machine Learning Is Supporting Decision-making.

Pancreas
This review critically analyzes how machine learning is being used to support clinical decision-making in the management of potentially resectable pancreatic cancer. Following PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-analyses...

Machine Learning Algorithm Helps Identify Non-Diagnosed Prodromal Alzheimer's Disease Patients in the General Population.

The journal of prevention of Alzheimer's disease
BACKGROUND: Recruiting patients for clinical trials of potential therapies for Alzheimer's disease (AD) remains a major challenge, with demand for trial participants at an all-time high. The AD treatment R and D pipeline includes around 112 agents. I...

PLATYPUS: A Multiple-View Learning Predictive Framework for Cancer Drug Sensitivity Prediction.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
Cancer is a complex collection of diseases that are to some degree unique to each patient. Precision oncology aims to identify the best drug treatment regime using molecular data on tumor samples. While omics-level data is becoming more widely availa...