HCAD 750 Module 5 study guide

HCAD 750 Module 5 study guide 1. the process of finding correlations or patterns among the data - facilitates data exploration - extract useful knowledge hidden in data: data mining 2. using patient data for any purpose beyond providing care for the individual patient brings with it some tricky issues regarding privacy, and keeping the information from falling into the wrong hands. There are significant legal issues related to the use of patient data in data mining efforts, specifically related to the de-identification, aggregation, and storage of the data. Failing to take the appropriate steps when using personal health data as a tool for population health could lead to serious consequences: HIPPA in relation to Data mining 3. -perform induction on the current data in order to make predictions.: Pre- dictive Data Mining 4. -ability for a device, machine, etc. to be able to take in numerous types of data and learn from the data in order to produce knowledge.: Meta-learning 5. - investigates how computers can learn based on data - automatically learn to recognize complex patterns and make intelligent decisions on their own based on the data: Machine Learning 6. refers to the process of reducing the inputs for processing and analysis, or finding the most meaningful inputs.: Feature Selection 7. - be applied to obtain a compressed representation of the data set that is much smaller in volume, yet maintains the integrity of the original data. - used when the data selected is too complex or huge: Data reduction 8. to request or seek out additional information on a specific subject. Makes the data more detailed.: drill down 9. - is an ensemble of models combined sequentially. - can be used to classify data - get a meta-learning device, stack the data in the device, the base learner is combined and produces the data information needed.: Stacking 10. - each of the data classifications are weighted. - once the system learns, it is able to continuously update and learn which ones are incorrect, and the weight shifts to reflect the accuracy: Boosting 11. - method used to increase accuracy with data mining - majority vote; more times a classification is picked, the more reliable the data. - algorithm creates an ensemble of models for learning scheme where each model gives an equally weighted prediction: Bagging (Bootstrap Aggregating) 12. DMAIC steps: define, measure, analyze, improve, and control - can explain why data behaves a certain way - not necessarily a data mining technique, but a model used to give more of answer to "why" and "how" in regard to data information. - adds additional steps to mining that yields better results: Six Sigma 13. is a term that describes the large volume of data - both structured and unstructured - that inundates a business on a day-to-day basis. But it's not the