Lecturer in Health Informatics
Swansea University Medical School
Telephone: (01792) 606292

Athanasios Anastasiou received his BEng in Biomedical Engineering from the Technological Education Institute of Athens – Greece in 2002 and his MRes in Communications Engineering and Signal Processing from the University of Plymouth – UK in 2004. His professional experience includes software development with a special interest in the medical domain and digital signal processing

Teaching

  • PM-339 Introduction to Health Data Science

    The module introduces students to the relatively new field of Health Data Science which deals with the systematic processing of healthcare data that can be found in a variety of different electronic formats. The course provides elements of computing and electronic databases that outline data access as well as elements of statistics that outline typical data processing operations.

  • PMIM102 Scientific Computing and Health Care

    The module aims at raising the awareness of students about scientific computing. It provides a brief overview of computation and focuses on the computational needs and workflows that health data scientists most often employ. Students will also learn about the professional context within which health data scientists operate.

  • PMIM202 Health Data Modelling

    Health data scientists are expected to work with diverse data sources. However, due to the abstraction offered by modern database management systems, these data sources can be treated similarly through a set of standardised operations. The objective of this module is to raise the awareness of students about the process of data modelling and the key operations involved in the data processing of large and diverse datasets.

  • PMIM302 Introductory Analysis of Linked Health Data

    This module introduces the topic of linked health data analysis at an introductory to intermediate level. It fills a gap in research training opportunities by combining the principles of health care epidemiology with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding in the classroom on each topic, followed by a training session on the corresponding computing solutions. Students use de-identified linked data files in the hands-on exercises. The computing component of the module assumes a basic familiarity with computing syntax used in programs such as SPSS, SAS or STATA and methods of basic statistical analysis of fixed-format data files.

  • PMIM402 Machine Learning in Healthcare

    Data scientists working in healthcare are called to deal with problems involving classification and pattern recognition. The objective of this module is to provide the essential theory and practical aspects of widely used machine learning software.

  • PMIM502 Health Data Visualisation

    Health data scientists making use of computational and storage resources will eventually be called to present their findings to an audience. The objective of this module is to enable students to choose and produce appropriate static and dynamic visualisations of health data using a range of media.

  • PMIM602 Advanced Analysis of Linked Health Data

    This module is taught at an intermediate to advanced level and assumes that students have completed PMIM302 Introductory Analysis of Linked Health Data or have equivalent knowledge. Advanced principles of health care epidemiology are combined with hands-on practical exercises in the implementation of computing solutions. The module provides students with a theoretical grounding on each topic, followed by a training session on the corresponding computing solutions. Students use de-identified data files in the hands-on exercises. The computing component of the module assumes a basic competence in the preparation of computing syntax for programs such as SPSS, SAS or STATA and familiarity with the statistical analysis of linked data files at an introductory to intermediate level.

  • PMIM702 Health Data Project

    This module builds on the knowledge and skills of modules PMIM302 and PMIM602 and are pre-requisites. Students will work independently with a large scale linked data set, developing and answering a specific research question.

  • PMIT100 Introduction to database systems with applications to health and social care

    This module introduces students to a range of electronic database systems used in the field of health and social care and basic theoretical concepts and terminology applicable to these systems. Students enrolling on this module will be introduced to a range of commonly used data models including the relational data model which will be used as a vehicle for further exploration of electronic data storage. Specific topics include, but are not limited to, healthcare data modeling and clinical encoding issues, transforming research questions into queries and exporting complex data for further processing by scientific computing software.

  • PMIT200 Introduction to Scientific Computation of health data

    This module introduces students to the process of developing algorithms to solve scientific problems and to the use of scientific computation software to answer research questions within the context of healthcare. Course participants will be exposed to the basic theory of computation and principles that are generally applicable to all aspects of computer programming but also specific concepts applicable to scientific computation software. The course will focus primarily on the imperative programming paradigm that is followed by the majority of scientific computation packages such as MATLAB, OCTAVE, SPSS, R, SAS, Python and others. The course will also cover the important aspects of code documentation and description via basic diagrams to enable students to integrate with large teams effectively.

  • PMIT300 Turning clinical information into coded data

    This module introduces students to the variety of clinical encoding systems used within the NHS and internationally as well as the tools used to review them and handle encoded data.