Information Extraction from Free Text Data in Health
About this course
In this MOOC, you will be introduced to advanced machine learning and natural language processing techniques to parse and extract information from unstructured text documents in healthcare, such as clinical notes, radiology reports, and discharge summaries. Whether you are an aspiring data scientist or an early or mid-career professional in data science or information technology in healthcare, it is critical that you keep up-to-date your skills in information extraction and analysis. To be successful in this course, you should build on the concepts learned through other intermediate-level MOOC courses and specializations in Data Science offered by the University of Michigan, so you will be able to delve deeper into challenges in recognizing medical entities in health-related documents, extracting clinical information, addressing ambiguity and polysemy to tag them with correct concept types, and develop tools and techniques to analyze new genres of health information. By the end of this course, you will be able to: Identify text mining approaches needed to identify and extract different kinds of information from health-related text data Create an end-to-end NLP pipeline to extract medical concepts from clinical free text using one terminology resource Differentiate how training deep learning models differ from training traditional machine learning models Configure a deep neural network model to detect adverse events from drug reviews List the pros and cons of Deep Learning approaches."
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What you'll learn
- Identify text mining approaches for health-related documents
- Create NLP pipelines for extracting medical concepts
- Differentiate between training deep learning and traditional machine learning models
- Configure deep neural networks for adverse event detection
- List pros and cons of deep learning approaches
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