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Data Science in Healthcare - 0990065

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  • Department: Hull York Medical School
  • Module co-ordinator: Dr. Lewis Paton
  • Credit value: 20 credits
  • Credit level: M
  • Academic year of delivery: 2024-25
    • See module specification for other years: 2023-24

Related modules

Pre-requisite modules

Co-requisite modules

  • None

Prohibited combinations

  • None

Additional information

Broadly speaking, this module assumes an understanding of programming using python and the basic principles of data analysis in python. 

For those students taking this module as part of the MSc Data Science, these skills will be covered by the core modules students will take in Semester 1, specifically:
Data Analysis and Machine Learning (module code CHE00045M)
Programming for Data Science (module code CHE00044M)
Skills for Data Scientists (module code CHE00048M)

 For students taking this module as part of a different programme of study, they will need to demonstrate an understanding of the broad pre-requisites outlined above.

Module will run

Occurrence Teaching period
A Semester 2 2024-25

Module aims

This module aims to provide an overview of the application of data science to medicine and healthcare. Building on existing theoretical data science skills, this module will explore the complex, interdisciplinary field of health data science. This module aims to enable students to:

  • Understand the ways data science is, and might be, used in healthcare.

  • Explore and apply a range of commonly used analytic techniques in healthcare.

  • Critically examine the use of data science in healthcare, by considering the limitations, risks, and ethical challenges in the field.

Module learning outcomes

By the end of this module, students will be able to:

  1. Critically appraise the main sources of data that arise in healthcare, and the differing roles these data sources play in health data science.

  2. Describe and critique the commonly used analytic approaches applied to healthcare data.

  3. Formulate a context appropriate data science research question.

  4. Select and apply appropriate analytic methods to healthcare data using python.

  5. Critically appraise the use of data science in a healthcare context and published studies in the field.

  6. Describe the main legal and ethical issues as they apply to data science in healthcare.

Module content

Module content will be delivered across three themes:

Theme 1: What is healthcare data science?
e.g., types of data, the hierarchy of evidence, potential benefits and challenges

Theme 2: Managing and analysing healthcare data.
e.g., missing data, rare outcomes, introductions to more advanced analytic approaches such as natural language processing

Theme 3: Health data science in practice
e.g., legal frameworks, ethical considerations, critical appraisal in a healthcare context

Assessment

Task Length % of module mark
Essay/coursework
Individual programming exercise and written report		 N/A 100

Special assessment rules

None

Additional assessment information

Formative completion of online activities will take place throughout the semester and will consist of i) coding activities, to assess data analytic techniques introduced in this module, and ii) question and answer exercises to assess factual and procedural knowledge.

Reassessment

None

Module feedback

Quantitative feedback and model answers for on-line tasks.

Written feedback on oral presentation and summative assessment, based on marking rubrics.

Indicative reading

Theme 1

  • Kubben, P., Dumontier, M. and Dekker. A. (2019). Fundamentals of Clinical Data Science. (2019). Springer Open.

  • Topol. E. (2019). Deep Medicine: How Artificial Intelligence Can Make Medicine Human Again. Basic Books.

Theme 2

  • Bishop, C. (2007). Pattern Recognition and Machine Learning. Springer.

  • Kuhn, M. and Johnson, K. (2013). Applied Predictive Modelling. Springer

  • Lane, H., Howard, C., and Hapke, H. M. (2019). Natural Language Processing in Action. Manning Publications Co.

Theme 3

  • Festor, P., Jia, Y., Gordon, A.C., Faisal, A.A., Habli, I. and Komorowski, M. (2022). Assuring the safety of AI- based clinical decision support systems: a case study of the AI Clinician for sepsis treatment. BMJ Health and Care Informatics. 29:e100549.

  • Naik, N., Zeeshan Hammed, B. M., Shetty, D. K. et al. (2022). Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility? Frontiers in Surgery. 9:862322.

  • Panch, T., Mattie, H. and Celi, L.A. (2019). The “inconvenient truth” about AI in healthcare. NPJ Digital Medicine. 2:77.


The information on this page is indicative of the module that is currently on offer. The University is constantly exploring ways to enhance and improve its degree programmes and therefore reserves the right to make variations to the content and method of delivery of modules, and to discontinue modules, if such action is reasonably considered to be necessary by the University. Where appropriate, the University will notify and consult with affected students in advance about any changes that are required in line with the University's policy on the Approval of Modifications to Existing Taught Programmes of Study.