14 December 2022
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Story André Damons
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Photo André Damons
Dr Michael Pienaar, Senior Lecturer and specialist in the UFS Department of Paediatrics and Child Health being presented to the acting Chancellor by his supervisor Prof Stephen Brown.
A lecturer from the University of the Free State (UFS) says the need to improve the care of seriously ill children is a vital part of reducing preventable deaths and diseases, and this led him to investigate the use of artificial neural networks to develop models for the prediction of patient outcomes in children with severe illness. The study was done for his PhD thesis.
This forms the basis for the PhD thesis of
Dr Michael Pienaar, Senior Lecturer and specialist in the
UFS Department of Paediatrics and Child Health, called, The Development and Validation of Predictive Models for Paediatric Critical Illness in Children in Central South Africa using Artificial Neural Networks. His thesis reports the development and testing of several machine learning models designed to help healthcare workers identify seriously ill children early in a range of resource-limited settings. Combining a systematic literature search and Delphi technique with clinical data from 1 032 participants, this research led to significant progress towards implementable models for community health workers in clinical practice.
Care for critically ill children is a mission and calling
Dr Pienaar graduated with a PhD specialising in Paediatrics on Monday (12 December) during the
Faculty of Health Sciences’ December graduation ceremony. It took him three years to complete this degree. His supervisor was
Prof Stephen Brown, Principal Specialist and Head of the Division of Paediatric Cardiology in the Department of Paediatrics and Child Health in the Faculty of Health Sciences at the UFS. Prof Nicolaas Luwes and Dr EC George were his co-supervisors.
“I have been working in paediatric critical care since 2019 and see the care of critically ill children as my mission and calling in life. At the outset of the project, I was interested in approaches to complex phenomena and wanted to investigate new methods for tackling these in healthcare.
“I have been interested in technology since childhood and in collaborating with other disciplines since I joined the university in 2019. Machine learning seemed like a great fit that could incorporate these interests and yield meaningful clinical results,” explains Dr Pienaar the reason why he chose this topic for his thesis.
He hopes that, in time, this work will lead to the implementation of integrated machine learning models to improve care and clinical outcomes for children in South Africa. From a scholarship perspective, he continues, his hope is that this work draws interest to this field in clinical research and encourages a move towards incorporating these new methods, as well as skills in areas such as coding and design in the armamentarium of a new generation of clinicians.
Medicine chooses you
According to Dr Pienaar, he always had broad interests, of which medicine is one. “I am very grateful to have found my way in medicine and am humbled and privileged to be allowed to walk with children and their families on a difficult and important journey. I believe this profession will choose you and put you where you are needed if you give it time and are prepared to listen.”
He describes graduating as a complicated ending to this period of his life and the beginning of a next chapter. He was humbled by the graduation ceremony.
“It was wonderful to graduate with undergraduates and postgraduates in my profession – I felt great pride and solidarity joining these new colleagues and specialists in taking the oath. I am certainly relieved, proud, excited, and happy. I am also very grateful to the university, my promotors, colleagues, friends, and family for supporting me through this process. I must confess, it is also slightly bittersweet, I loved working on this and do miss it, but look forward to the next exciting project.
“I would like to thank my Head of Department, Dr (Nomakhuwa) Tabane, my supervisors, my family and friends once again. I would also like to acknowledge and thank the
National Research Foundation (NRF) as well as the University of the Free State for their assistance with funding this research.”