A Prototype Agent Based Model and Machine Learning Hybrid System for Healthcare Decision Support

2012 ◽  
pp. 230-253 ◽  
Author(s):  
Marek Laskowski
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Decision Process ◽  
Patient Flow ◽  
Policy Decision ◽  
Healthcare Policy ◽  
Learning System ◽  
Agent Based Model ◽  
Agent Based ◽  
Markov Decision

Science is on the verge of practical agent based modeling decision support systems capable of machine learning for healthcare policy decision support. The details of integrating an agent based model of a hospital emergency department with a genetic programming machine learning system are presented in this paper. A novel GP heuristic or extension is introduced to better represent the Markov Decision Process that underlies agent decision making in an unknown environment. The capabilities of the resulting prototype for automated hypothesis generation within the context of healthcare policy decision support are demonstrated by automatically generating patient flow and infection spread prevention policies. Finally, some observations are made regarding moving forward from the prototype stage.

A Prototype Agent Based Model and Machine Learning Hybrid System for Healthcare Decision Support

2011 ◽  
Vol 2 (4) ◽  
pp. 67-90 ◽  
Author(s):  
Marek Laskowski
Keyword(s):  
Machine Learning ◽  
Decision Support ◽  
Patient Flow ◽  
Policy Decision ◽  
Healthcare Policy ◽  
Agent Based Modeling ◽  
Learning System ◽  
Agent Based Model ◽  
Agent Based ◽  
Markov Decision

Science is on the verge of practical agent based modeling decision support systems capable of machine learning for healthcare policy decision support. The details of integrating an agent based model of a hospital emergency department with a genetic programming machine learning system are presented in this paper. A novel GP heuristic or extension is introduced to better represent the Markov Decision Process that underlies agent decision making in an unknown environment. The capabilities of the resulting prototype for automated hypothesis generation within the context of healthcare policy decision support are demonstrated by automatically generating patient flow and infection spread prevention policies. Finally, some observations are made regarding moving forward from the prototype stage.

scholarly journals Streaming Video Classification Using Machine Learning

2020 ◽  
Vol 17 (4A) ◽  
pp. 677-682
Author(s):  
Adnan Shaout ◽  
Brennan Crispin
Keyword(s):  
Machine Learning ◽  
Decision Process ◽  
Data Stream ◽  
Streaming Video ◽  
Learning System ◽  
Network Interface ◽  
Video Classification ◽  
Design And Implementation ◽  
Markov Decision ◽  
Streaming Services

This paper presents a method using neural networks and Markov Decision Process (MDP) to identify the source and class of video streaming services. The paper presents the design and implementation of an end-to-end pipeline for training and classifying a machine learning system that can take in packets collected over a network interface and classify the data stream as belonging to one of five streaming video services: You Tube, You Tube TV, Netflix, Amazon Prime, or HBO

scholarly journals Implementation of modified SARSA learning technique in EMCAP

2017 ◽  
Vol 7 (1.5) ◽  
pp. 274
Author(s):  
D. Ganesha ◽  
Vijayakumar Maragal Venkatamuni
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Decision Process ◽  
Learning Algorithm ◽  
Research Work ◽  
Learning System ◽  
State Action ◽  
Learning Technique ◽  
Markov Decision ◽  
Experiment Analysis

This research work presents analysis of Modified Sarsa learning algorithm. Modified Sarsa algorithm.  State-Action-Reward-State-Action (SARSA) is an technique for learning a Markov decision process (MDP) strategy, used in for reinforcement learning int the field of artificial intelligence (AI) and machine learning (ML). The Modified SARSA Algorithm makes better actions to get better rewards.  Experiment are conducted to evaluate the performace for each agent individually. For result comparison among different agent, the same statistics were collected. This work considered varied kind of agents in different level of architecture for experiment analysis. The Fungus world testbed has been considered for experiment which is has been implemented using SwI-Prolog 5.4.6. The fixed obstructs tend to be more versatile, to make a location that is specific to Fungus world testbed environment. The various parameters are introduced in an environment to test a agent’s performance. This modified   SARSA learning algorithm can   be more suitable in EMCAP architecture.  The experiments are conducted the modified   SARSA Learning system gets   more rewards compare to existing  SARSA algorithm.

Rule-Based Ontology Decision Model

2016 ◽  
pp. 271-291
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Rule Based ◽  
The Matrix ◽  
Markov Decision ◽  
Minimization Technique ◽  
Complex Decision

In chapter 7, we examined some selected case study applications of some decision support systems. Those considered were the matrix-based used in determining labour cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique and Markov decision process. As earlier discussed, our focus will be on rule-based decision support systems. This is because rule-based systems are more encompassing and can easily be employed to deal with complex decision about construction activities. Hence in this chapter, an overview of rule-based decision system will be examined.

Some Selected Case Study Applications

2016 ◽  
pp. 180-270
Keyword(s):  
Decision Support ◽  
Decision Support Systems ◽  
Decision Process ◽  
Support Systems ◽  
Labor Cost ◽  
The Matrix ◽  
Markov Decision ◽  
Decision Support Models ◽  
Minimization Technique

Having examined the modelling principles of underpinning based decision support systems applied to construction in Chapter 6, this chapter will now demonstrate their detail applications in construction practice. Specifically, 7 decision-support systems will be examined. The choices are based on the fact that data for use in the decision support models are available. The decision-support systems considered are the matrix-based used in determining labor cost, sub-chaining method, linear regression, optimization (i.e. minimization) technique, Markov decision process and rule-based systems.

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