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Assignment: Artificial Intelligence and Pattern Recognition

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Assignment: Artificial Intelligence and Pattern Recognition
MS6534 Information and Communication Technology Management
Assignment 3 (Individual)

Please read CLOSING CASE STUDY TWO: CLOSING THE GREAT HEALTH CARE DIVIDE WITH PATTERN RECOGNITION AND DATA-MINING TECHNOLOGIES (Ch.4 p.184) and provide short discussions to the following questions.

In this second closing case study, you will explore how the health care industry is using decision support and artificial intelligence technologies to provide better and more affordable healthcare.

QUESTIONS

1. In the case study, we referred to the systems being developed and used as decision support systems. However, we also identified various artificial intelligence (AI) technologies as well. How can a decision support system incorporate and use AI technologies such as pattern recognition?

ARTIFICIAL INTELLIGENCE (AI) tools and techniques can aid in the diagnosis of disease states and assessment of treatment outcomes, so AI can be used by a decision support system as pattern recognition to analyze healthcare data and generate a representation of knowledge and make a decision support. One of this AI technologies tools is the Artificial Neural Networks which work much like the human brain and have the ability to learn from training data and store knowledge in the network. In the learning phase, it maps relation between inputs and the corresponding expected outputs. Neural networks are frequently used as diagnostics, and therefore it is important to have good generalization ability, that is, good performance in predicting results in response to unseen data. Neural networks have found the majority of their applications in time-series prediction, signal processing, financial forecasting and especially in health care as pattern recognition.

2. At the Mayo Clinic, patients are given opt in and opt out rights concerning whether or not their information is used in the system that determines the most appropriate therapies given the specific patient profile. So far, 95

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