18746289. LEARNING SYSTEM OF MACHINE LEARNING MODEL FOR PREDICTION OF STAY LENGTH IN HOSPITAL simplified abstract (NEC Corporation)
Contents
LEARNING SYSTEM OF MACHINE LEARNING MODEL FOR PREDICTION OF STAY LENGTH IN HOSPITAL
Organization Name
Inventor(s)
Mischa Schmidt of Heidelberg (DE)
Julia Gastinger of Heidelberg (DE)
LEARNING SYSTEM OF MACHINE LEARNING MODEL FOR PREDICTION OF STAY LENGTH IN HOSPITAL - A simplified explanation of the abstract
This abstract first appeared for US patent application 18746289 titled 'LEARNING SYSTEM OF MACHINE LEARNING MODEL FOR PREDICTION OF STAY LENGTH IN HOSPITAL
The abstract describes a method for automated machine learning that involves running multiple instances of different automated machine learning frameworks simultaneously to train machine learning models and compute their performance scores for a given task.
- The method controls the execution of multiple automated machine learning frameworks on a machine learning task, considering available computational resources and time constraints.
- During the execution, multiple machine learning models are trained, and their performance scores are calculated.
- Based on the performance scores, one or more of the trained machine learning models can be selected for the task.
- This innovation can be applied in various fields such as predicting patient discharge and predictive control in buildings for energy optimization.
Potential Applications: - Predicting patient discharge - Predictive control in buildings for energy optimization - Automated data analysis in various industries
Problems Solved: - Efficient utilization of computational resources - Automated selection of the best machine learning models for a given task
Benefits: - Time-saving in model selection process - Improved accuracy in machine learning tasks - Enhanced efficiency in resource allocation
Commercial Applications: Title: Automated Machine Learning for Enhanced Predictive Analytics This technology can be used in healthcare, energy management, finance, and other industries for automated data analysis and predictive modeling.
Questions about Automated Machine Learning: 1. How does automated machine learning improve the efficiency of model selection? Automated machine learning streamlines the process by running multiple frameworks simultaneously and selecting the best-performing models based on their performance scores.
2. What are the key benefits of using automated machine learning in predictive analytics? Automated machine learning saves time, improves accuracy, and optimizes resource allocation in machine learning tasks.
Original Abstract Submitted
A method for automated machine learning includes controlling execution of a plurality of instantiations of different automated machine learning frameworks on a machine learning task each as a separate arm in consideration of available computational resources and time budget. During the execution by the separate arms, a plurality of machine learning models are trained and performance scores of the plurality of trained machine learning models are computed such that one or more of the plurality of trained machine learning models are selectable for the machine learning task based on the performance scores. This invention can be used for predicting patient discharge, predictive control in buildings for energy optimization, and so on.
