This module is the second module within the learning Data science in Healthcare, and builds on module 1 ‘Data and technology in healthcare’. The module is organised around a real-life clinical example/ problem. Students are trained to obtain and process "rich data" based on relevant (medical) data and data sources in healthcare: to choose and implement machine learning algorithms: to solve clinical or health problems and challenges. Given a specific question, students will first look at what types of data are needed and what requirements must be set to assure good data quality. Based on the fundamentals from Module 1, AI algorithms are discussed and their specific advantages, disadvantages and most appropriate use cases are presented. The focus will be on the thorough understanding of methods as opposed to an exhaustive list of all available algorithms.

The specific course objectives are;

Expert:

The student is able to:

        ● Identify a type of learning problem for realistic clinical  application Propose a good first strategy to develop an AI  approach for a clinical application

        ● Judge the reliability of the benchmarks  performed for an AI healthcare solution

        ● Evaluate the trustworthiness of AI claims.

        ● See how choice for algorithms and training data  might produce biases

        ● Propose solutions to bridge AI ideas to clinical  relevance

Investigator

The student is able to: 

        ● Know when advanced algorithms are likely to bring  added benefit.

        ● Interpret differences in various metric for their  realistic impact