Course: Introduction to Clinical Data
Modules:
Welcome, Introduction to the data mining workflow, Real Life Example, Example: Finding similar patients, Example: Estimating risk, Putting patient data on timeline, Revisit the data mining workflow steps, Types of research questions, Research questions suited for clinical data, Example: making decision to treat, Properties that make answering a research question useful, Wrap Up
Review of the healthcare system, Review of key entities and the data they collect, Actors with different interests, Common data types in Healthcare, Strengths and weaknesses of observational data, Bias and error from the healthcare system perspective, Bias and error of exposures and outcomes, How a patient's exposure might be misclassified, How a patient's outcome could be misclassified, Electronic medical record data, Claims data, Pharmacy, Surveillance datasets and Registries, Population health data sets, A framework to assess if a data source is useful, Wrap Up
Introduction, Time, timelines, timescales and representations of time, Timescale: Choosing the relevant units of time, What affects the timescale, Representation of time, Time series and non-time series data, Order of events, Implicit representations of time, Different ways to put data in bins, Timing of exposures and outcomes, Clinical processes are non-stationary, Wrap Up
Turning clinical data into something you can analyze, Defining the unit of analysis, Using features and the presence of features, How to create features from structured sources, Standardizing features, Dealing with too many features, The origins of missing values, Dealing with missing values, Summary recommendations for missing values, Constructing new features, Examples of engineered features, When to consider engineered features, Main points about creating analysis ready datasets, Structured knowledge graphs, So what exactly is in a knowledge graph, What are important knowledge graphs, How to choose which knowledge graph to use, Wrap Up
Introduction to unstructured data, What is clinical text, The value of clinical text, What makes clinical text difficult to handle, Privacy and de-identification, A primer on Natural Language Processing, Practical approach to processing clinical text, Summary - Clinical text, Overview and goals of medical imaging, Why are images important?, What are images?, A typical image management process, Summary - Images, Overview of biomedical signals, Why are signals important?, What are signals?, What are the major issues with using signals?, Summary - Signals, Wrap Up
Introduction to electronic phenotyping, Challenges in electronic phenotyping, Specifying an electronic phenotype, Two approaches to phenotyping, Rule-based electronic phenotyping, Examples of rule based electronic phenotype definitions, Constructing a rule based phenotype definition, Probabilistic phenotyping, Approaches for creating a probabilistic phenotype definition, Software for probabilistic phenotype definitions, Wrap Up
Introduction to Research Ethics and AI, The Belmont Report: A Framework for Research Ethics, Ethical Issues in Data sources for AI, Secondary Uses of Data, Return of Results, AI and The Learning Health System, Ethics Summary
Conclusion