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14 December 2022 | Story André Damons | 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.”

News Archive

Fight against Ebola virus requires more research
2014-10-22

 

Dr Abdon Atangana
Photo: Ifa Tshishonge
Dr Abdon Atangana, a postdoctoral researcher in the Institute for Groundwater Studies at the University of the Free State (UFS), wrote an article related to the Ebola virus: Modelling the Ebola haemorrhagic fever with the beta-derivative: Deathly infection disease in West African countries.

“The filoviruses belong to a virus family named filoviridae. This virus can cause unembellished haemorrhagic fever in humans and nonhuman monkeys. In literature, only two members of this virus family have been mentioned, namely the Marburg virus and the Ebola virus. However, so far only five species of the Ebola virus have been identified, including:  Ivory Coast, Sudan, Zaire, Reston and Bundibugyo.

“Among these families, the Ebola virus is the only member of the Zaire Ebola virus species and also the most dangerous, being responsible for the largest number of outbreaks.

“Ebola is an unusual, but fatal virus that causes bleeding inside and outside the body. As the virus spreads through the body, it damages the immune system and organs. Ultimately, it causes the blood-clotting levels in cells to drop. This leads to severe, uncontrollable bleeding.

Since all physical problems can be modelled via mathematical equation, Dr Atangana aimed in his research (the paper was published in BioMed Research International with impact factor 2.701) to analyse the spread of this deadly disease using mathematical equations. We shall propose a model underpinning the spread of this disease in a given Sub-Saharan African country,” he said.

The mathematical equations are used to predict the future behaviour of the disease, especially the spread of the disease among the targeted population. These mathematical equations are called differential equation and are only using the concept of rate of change over time.

However, there is several definitions for derivative, and the choice of the derivative used for such a model is very important, because the more accurate the model, the better results will be obtained.  The classical derivative describes the change of rate, but it is an approximation of the real velocity of the object under study. The beta derivative is the modification of the classical derivative that takes into account the time scale and also has a new parameter that can be considered as the fractional order.  

“I have used the beta derivative to model the spread of the fatal disease called Ebola, which has killed many people in the West African countries, including Nigeria, Sierra Leone, Guinea and Liberia, since December 2013,” he said.

The constructed mathematical equations were called Atangana’s Beta Ebola System of Equations (ABESE). “We did the investigation of the stable endemic points and presented the Eigen-Values using the Jacobian method. The homotopy decomposition method was used to solve the resulted system of equations. The convergence of the method was presented and some numerical simulations were done for different values of beta.

“The simulations showed that our model is more realistic for all betas less than 0.5.  The model revealed that, if there were no recovery precaution for a given population in a West African country, the entire population of that country would all die in a very short period of time, even if the total number of the infected population is very small.  In simple terms, the prediction revealed a fast spread of the virus among the targeted population. These results can be used to educate and inform people about the rapid spread of the deadly disease,” he said.

The spread of Ebola among people only occurs through direct contact with the blood or body fluids of a person after symptoms have developed. Body fluid that may contain the Ebola virus includes saliva, mucus, vomit, faeces, sweat, tears, breast milk, urine and semen. Entry points include the nose, mouth, eyes, open wounds, cuts and abrasions. Note should be taken that contact with objects contaminated by the virus, particularly needles and syringes, may also transmit the infection.

“Based on the predictions in this paper, we are calling on more research regarding this disease; in particular, we are calling on researchers to pay attention to finding an efficient cure or more effective prevention, to reduce the risk of contamination,” Dr Atangana said.


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