Resource intensive applications, such as speech recognition, natural language processing, computer vision and graphics, machine learning, augmented reality, planning, and decision-making become common in MDs.
The automatic generation of individualized plans in specific domains is an open problem that combines aspects related to automated planning, machine learning or recommendation systems technology.
The ideas of intelligent backtracking (IB) and explanation-based learning (EBL) have developed independently in the constraint satisfaction, planning, machine learning and problem solving communities.
First, it's added automated hyperlinking between blueprints stored on the app, basically using machine learning to figure out which plans refer to one another and then creating links between them.
Here's one example: FitGenie, an iOS app whose ex-Georgia Tech co-founders bill it as a "smart calorie counter" — on account of applying machine learning algorithms to simplify nutrition planning for people wanting to achieve a certain weight or fitness goal.
In this paper, we are continuing to investigate the potential for machine learning algorithms to guide rehabilitation planning for home care clients.
We believe our work to date supports continued investigation of the potential for advanced statistical techniques, including machine learning algorithms, to support care planning for rehabilitation.
Flake plans to add machine learning, which improves each search by drawing on past efforts.
Moving forward, H2O plans to make machine learning even more automatic.
IBM plans to bring machine learning to other data sources sitting inside data centers over time.
