28 December 2020 | Story André Damons | Photo Supplied
Dr Michael Pienaar is a lecturer in the University of the Free State’s (UFS) department of Paediatrics and Child Health.

A lecturer from the University of the Free State’s (UFS) department of Paediatrics and Child Health is investigating the use of artificial neural networks to develop models for the prediction of patient outcomes in children with severe illness.

Dr Michael Pienaar, senior lecturer and specialist, is conducting this research as part of his doctoral research and the study deals primarily with the development of models that are designed and calibrated for use in South Africa. These artificial neural networks are computer programs designed to mimic some of the learning characteristics of biological neurons.

The potential applications of models

According to Dr Pienaar these models have traditionally been developed in high-income nations using conventional statistical methods.

“The potential applications of such models in the clinical setting include triage, medical research, guidance of resource allocation and quality control. Having initially begun this research investigating the prediction of mortality outcomes in the paediatric intensive care unit (PICU) I have broadened my scope to patients outside of PICU, seeking to identify children early during their illnesses who are at risk of serious illness requiring PICU,” says Dr Pienaar.

The research up until now has been directed towards the identification of characteristics that are both unique to children with serious illness in South Africa, but also accessible to clinicians in settings where expertise and technical resources are limited.

Research still in the early changes

The research is still in its early stages but next year a series of expert review panels will be held to investigate the selection of variables for the model, after which the collection of clinical data will begin. Once the data has been collected and prepared, a number of candidate models will be developed and evaluated. This should be concluded by the end of 2022.

Says Dr Pienaar: “The need to engage with the rapid proliferation of technology, particularly in the realms of machine learning, mobile technology, automation and the Internet of Things is as great in medical research now as it is in any academic discipline.

“It is critical that research, particularly in South Africa, engage with this in order to take advantage of the opportunities offered and avoid the dangers that go paired with them. Together with the technology as such, it has been essential to pursue this project as an interdisciplinary undertaking involving clinicians, biostatisticians and computer engineers.”

Hope for the research  

Dr Pienaar says he was very fortunate and grateful to be the recipient of a generous interdisciplinary grant from the UFS which has allowed him to procure software and equipment that is critical to this project.

“The hope for this research is that the best performing of these models can be integrated with a mobile application that assists practitioners in a wide range of settings in the identification, treatment and early referral of children at high risk of severe illness. I would like to expand this research project to include other countries in Africa and South America and to use it as a bridge to collaboration with other clinical researchers in the Global South,” says Dr Pienaar.

As an early career researcher, Dr Pienaar hopes that this research can serve as a platform to build a body of research that uses the rapid technological advances of these times together with a wide range of collaborations with other disciplines in the pursuit of better child health.

He concludes by saying that he has had excellent support thus far from his supervisors, Prof Stephen Brown (Faculty of Health Sciences, UFS), Dr Nicolaas Luwes (Faculty of Computer Science and Engineering, Central University of Technology) and Dr Elizabeth George (Medical Research Council Clinical Trials Unit, University College London). I have also been supported by the Robert Frater Institute in the Faculty of Health Sciences.

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