8.G. Skills building seminar: AI for public health dummies – overview and a benchmark algorithm: Random Forests
Authors:   Organised by: Unisanté (Switzerland), EUPHA-DH,   Chair persons: Monica Brinzac (EUPHAnxt), Stefan Buttigieg (EUPHA-DH)
Published: October 2021

Added value

The development of digital health is crucial to the improvement of health care services and systems, and AI / Machine learning (ML) is undoubtedly a significant part of it. AI has brought tremendous improvements in multiple domains, particularly in medical imaging or biosignal detection, and is currently under development for decision-making aid and early diagnostic prediction (precision medicine). However, it is often seen as an overly complicated set of methods that few people understand and do not offer many advantages over traditional methods for addressing real-life problems and may even pose risks to health system users. In this regard, on April 21, 2021, the European Commission adopted a proposal for a regulation (the AI Regulation) on ‘artificial intelligence systems,' which it describes as ‘the first-ever legal framework on AI.' According to the AI Regulation, an AI system is a ‘software that is developed with one or more of [certain] approaches and techniques ... and can, for a given set of human-defined objectives, generate outputs such as content, predictions, recommendations, or decisions influencing the environments they interact with.' For public health professionals, the challenges are in understanding the key concepts and technical aspects of AI and identifying its most relevant applications in terms of impact on population health and health systems. This skill-building seminar aims to exhibit and discuss the usefulness of AI methods in public health and introduce its main concepts and frameworks as well as some important methods and algorithms. In order to transfer practical skills to the participants, we will introduce a particular algorithm, Random Forests, focusing on its main theoretical ideas and how to use it in practice for modeling (with Python and R languages). We will also present practical cases of random forest use in public health and, finally, discuss their many advantages and potential limitations compared to more traditional statistical methods.

Coherence between the presentations & workshop topic

We will discuss the importance of AI and machine learning within the digital health domain and its potential benefits, particularly concerning predictive medicine and health care services and systems' improvement. We will present real-life case studies using the capabilities of random forests for a variety of modeling tasks. Finally, we will seek to improve the skills of public health professionals in AI, particularly in machine learning, through the transfer of theoretical and practical (coding) knowledge.

Format of the workshop

The seminar will be composed of two presentations, each lasting 20-25 minutes. We will interact with the participants about the practical and valuable aspects of the methods presented during the presentations. After/Before the presentation, we will foster a discussion about the usefulness of these methods in the public health field.

Key messages

The use of AI could be very beneficial to digital public health for decision-making and prediction purposes – as long as efforts are made to grasp its concepts and abilities. Random Forests is a powerful, easy to use, and volatile method, offering valuable aid to traditional statistical methods for various complex tasks and difficult data handling situations.