Define essential terminology in text mining and predictive analytics. Explain how NLP converts unstructured text into features suitable for modeling. Apply predictive modeling techniques to clinical text data. Analyze health data trends to support decision-making. Evaluate predictive model accuracy and ethical implications. Design an end-to-end predictive analytics framework using clinical text for improved healthcare outcomes.

Foundational knowledge of NLP concepts and text data processing. Basic understanding of statistics and machine learning (regression, classification). Familiarity with EHR systems and clinical data structures. Introductory programming experience in Python or use of analytical tools (e.g., R, RapidMiner, or KNIME).

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Health Informatics Professionals interested in predictive modeling and AI integration. Data Scientists and Analysts seeking to specialize in healthcare text analytics. Clinical Researchers exploring data-driven methods for disease prediction. Healthcare IT Professionals aiming to build predictive tools for clinical decision support. Graduate Students in bioinformatics, AI, or health data science programs.