Course Aim

The course provides a comprehensive introduction to trial design features used to mitigate bias, important aspects of trial design, conduct, analysis and reporting, and challenges and solutions for conducting RCTs with some focus on behavioural interventions. This will include some coverage of methods for elucidation of treatment mechanisms (e.g. mediation). Throughout the course the emphasis will be on practical issues faced by researchers in the conduct and analysis of RCTs through the lens of the mental health setting, and participants will be provided with skills to design, conduct and analyse rigorous RCTs in this research area.

Requirements 

This course requires an application to be made, please see more information tab on how to apply. Some practical sessions will involve analyses and interpretation using practice data sets using the Stata and/or R programs.  Stata and R syntax will be provided and explained, but some familiarity with software, especially Stata, is necessary, and individuals should have some knowledge of fitting regression models. Successful participants will need to bring their own laptop computer to the course with working installations of: 1) Stata version 14 or higher, 2) the R program.

COURSE OVERVIEW

This course is run over a one-month period (2 x three-hour sessions per week), followed by a workshop with invited speakers, and is aimed at PhD students and researchers working in applied psychometrics. The course provides a comprehensive introduction to the fundamental ideas of psychometric theory and their implementation and is taught by Dr Silia Vitoratou and Prof Andrew Pickles.

Live remote access of in-person sessions will be available for those who cannot attend on campus.

COURSE DESCRIPTION

Starting from the scale construction and gradually moving to the most recent statistical methods employed in measurement, the course provides a complete methodological framework for applied researchers. While the classical test theory ideas on reliability and validity, often used in the literature, are presented, the more methodologically sound item response theory counterparts are explained (difficulty and discrimination, item and total characteristic and information curves).

For the measurement of the latent variable(s) the course presents exploratory and confirmatory factor analysis (EFA and CFA) for numerical data. For categorical data, both the item response theory approach (IRT; 2-parameter logistic, grated response and partial credit models) as well as the item factor analysis model (IFA; both with regard to EFA and CFA). The course also presents the methods to explore measurement differences (measurement invariance) between groups (MG CFA), measurement differences due to covariates (MIMIC models), and measurement differences between raters or time points (longitudinal IFA), for all types of data.

Learning Outcomes. Subject-specific: Knowledge, Understanding and Skills

At the end of the course the students should be able to demonstrate subject-specific knowledge, understanding and skills and have the ability to:

• Develop an understanding of the underlying principles of artificial intelligence.
• Identify and describe different intelligent agents and their operating environments
• Understand different problem-solving and planning strategies, with a particular focus on constraint satisfaction problems and multi-agent planning.
• Justify and critique the use of different learning methods for medical and healthcare applications, in particular those used in temporal reasoning tasks.
• Be familiar with the principles of reinforcement learning and other goal-oriented algorithms.
• Be able to develop simple AI techniques, assess their quality and validate them using Python software.

Learning Outcomes. General: Knowledge, Understanding and Skills

On successful completion of this module the student should be able to:

• Study autonomously and undertake activities/projects with minimum guidance.
• Effectively communicate and critically assess own work in discussion groups, online forums and presentations.
• Read, understand and critique an AI-healthcare application in the research literature.
• Successfully work in a team during computer group lab sessions.
• Show confidence in the use of Python to implement AI techniques.

Requirements:

• Introduction to Health Informatics (7PAVITHI)
• This workshop will assume that participants have a basic knowledge of the high-level programming language, python. Participants will need their own laptop/computer with python installed.

Course Aim:

The course provides an introduction to the nature of medical text, and the technical and organisational challenges encountered when processing.

The course will be based around practical examples and widely used NLP tools.

The course aims to provide an introduction to the major techniques of natural language processing, and to provide participants with the skills to create their own NLP applications, using both rules based and statistical approaches. Methods will be introduced for extracting structured information from text, and for automatically classifying text, together with the selection of data for training and for evaluation.

The course will discuss aspects of NLP that are specific to medical and biomedical texts, and develop a critical awareness of the issues associated with these texts. Participants will be equipped with the skills needed to analyse their own language processing problems, to design and test solutions, and to understand the limitations of those solutions.

The course will provide a practical instruction in the use of some widely used tools in NLP, including nltk (a Python toolkit).

Learning Outcomes: Subject specific: Knowledge, Understanding and Skills

On successful completion of this module the student should be able to

• Understand the complexities of language/data
• Explain when and why NLP is appropriate.
• Interpret NLP paradigms in new and complex contexts to assess their suitability.
• Apply or implement a NLP paradigm to a specific problem.
• Demonstrate deeper analytical skills by
  • Showing an understanding of different evaluation techniques
  • Using their knowledge to evaluate their practice in terms of the NLP model or method used.