18188990. SYSTEMS AND METHODS FOR AUTOMATIC BENCHMARKING FOR RADIOLOGY simplified abstract (GE Precision Healthcare LLC)
Contents
SYSTEMS AND METHODS FOR AUTOMATIC BENCHMARKING FOR RADIOLOGY
Organization Name
Inventor(s)
Philippe Gerner of Strasbourg (FR)
María del Pilar Pujadas of Madrid (ES)
Hugo Laullier of Montpellier (FR)
SYSTEMS AND METHODS FOR AUTOMATIC BENCHMARKING FOR RADIOLOGY - A simplified explanation of the abstract
This abstract first appeared for US patent application 18188990 titled 'SYSTEMS AND METHODS FOR AUTOMATIC BENCHMARKING FOR RADIOLOGY
The patent application describes a medical system that includes a client device with a graphical user interface and a display device, connected to a network, and an analytics tool that can receive user inputs, identify values of variables of interest from a subject, generate causal graphs illustrating influences of study variables on the identified values, and update the graphs based on user inputs.
- The system includes a client device with a graphical user interface and a display device.
- An analytics tool is configured to receive user inputs and identify values of variables of interest from a subject.
- The tool can generate causal graphs illustrating influences of study variables on the identified values.
- The system can automatically update the causal graphs based on user inputs to adjust study variables.
Potential Applications: - Medical research and analysis - Clinical decision support systems - Healthcare data visualization
Problems Solved: - Providing a visual representation of the influences of study variables on specific values of interest - Automating the updating process of causal graphs based on user inputs
Benefits: - Enhanced understanding of complex medical data - Improved decision-making in healthcare settings - Efficient analysis of variables of interest in medical research
Commercial Applications: Title: "Advanced Medical Data Analysis System" This technology could be used in medical research institutions, hospitals, and healthcare organizations to improve data analysis and decision-making processes. It could also be valuable for pharmaceutical companies conducting clinical trials.
Prior Art: Researchers in the field of medical informatics and data visualization have explored similar concepts in the past. Studies on causal relationships in healthcare data and the use of graphical representations for analysis can be found in academic literature.
Frequently Updated Research: As medical data analysis techniques continue to evolve, researchers are exploring new methods for visualizing complex datasets and improving the accuracy of predictive models in healthcare settings.
Questions about Medical Data Analysis Systems: 1. How does this system improve upon traditional methods of medical data analysis? This system enhances traditional methods by providing visual representations of causal relationships between study variables and values of interest, allowing for more intuitive data interpretation.
2. What are the potential limitations of using causal graphs in medical research and analysis? Causal graphs may oversimplify complex relationships in medical data, leading to potential biases or inaccuracies in the analysis. Researchers should carefully consider the context and limitations of causal graph representations in their studies.
Original Abstract Submitted
Various methods and systems are provided for a medical system, comprising a client device having a graphical user interface (GUI) and a display device, the client device operably coupled to a network, and an analytics tool configured with instructions stored on a memory and executable by a processor to receive a first user input via the client device, identify values of at least one variable of interest (VOI) from a subject of a benchmark target variable which differ by at least a first threshold amount from other values of the same VOI from other subjects of the benchmark target variable within a benchmarking context, generate and output for display on the display device a causal graph illustrating influences of different study variables on an identified VOI value, and automatically update the causal graph and display thereof based on a second user input to adjust at least one study variable.
