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In the context of emerging technologies in AI, a healthcare organization is considering implementing a machine learning model to predict patient readmission rates. The organization has access to a variety of data sources, including electronic health records (EHR), patient demographics, and historical readmission data. They are particularly interested in understanding how different algorithms might impact the accuracy of their predictions. Which of the following approaches would most effectively enhance the model\'s predictive performance while ensuring compliance with data privacy regulations such as HIPAA?
Utilizing ensemble learning techniques that combine multiple models to improve accuracy while applying differential privacy measures to protect patient data.
Implementing a single decision tree model that uses all available data without any privacy considerations.
Relying solely on historical readmission data without incorporating patient demographics or EHR, as it simplifies the model.
Using a neural network with a large number of parameters but without any regularization techniques to avoid overfitting.

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