December 14, 2023. Thursday, 3PM. TG23 (Town Hall)
Speaker: Olugbenga Oluwagbemi (Middlesex)
Title: Voice-enabled fuzzy logic-based HIV diagnostic system with indigenous multilingual interfaces
Abstract: HIV still constitutes a major public health problem globally. According to the World Health Organization (WHO), in year 2022, 630 000 [480 000–880 000] people died from HIV-related causes and 1.3 million [1.0–1.7 million] people acquired HIV. The highest prevalence of HIV is in South Africa. Access to HIV diagnostic and testing services is either not readily available to many rural and sub-urban dwellers of South Africa. In many rural regions and some sub-urban regions of South Africa, for instance, the most widely used languages include Afrikaans, Zulu and Xhosa, with only limited comprehension in English language. In the absence of interpreters and HIV test kits, due to limited resources, English-speaking medical doctors, nurses, and foreign medical personnel, often find it difficult to efficiently communicate in Afrikaans, Zulu and Xhosa with indigenous ethnic patients at rural clinics for HIV diagnosis. Such language barriers have the potential to cause frustration, miscommunication, time-wastage and in extreme cases, delayed diagnoses, and treatments
Here, we developed a voice-enabled fuzzy logic HIV diagnostic system with indigenous multilingual interfaces for South Africa to facilitate HIV diagnosis in underprivileged rural and sub-urban South African communities. We provide examples on how the tool can be applied towards HIV diagnosis by using existing data from scientific literature and publicly available data sources. We hope this tool would help health care practitioners working in indigenous communities of many South African communities, to efficiently diagnose HIV and ultimately control its transmission. In addition, we hope the tool would help address healthcare disparities and reduce gaps in healthcare quality delivery between different South African communities, thus helping to promote equality, inclusion and develop more inclusive healthcare framework. In the future, other variants of this tool will be developed and applied to other Sub-Saharan countries with high HIV prevalence.
Bio: Olugbenga (‘Olu’) Oluwagbemi received his Ph.D. degree in Computer Science in 2013, and MSc and BSc degrees in Computer Science, in 2004 and 1999, respectively. He is currently a Senior Lecturer in the Department of Computer Science at Middlesex University, United Kingdom. Before joining Middlesex University, he worked as Associate Professor of Computer Science and IT in Sol Plaatje University, South Africa from 2020 to 2023, and previously as Senior Lecturer of Computer Science in the University of the Western Cape, South Africa, Federal University Lokoja, Nigeria and Covenant University Nigeria, respectively, from 2014 to 2020, and Lecturer of Computer Science in Covenant University from 2006 to 2014. He was a DST-NRF Innovation postdoctoral researcher with the Ecological Modelling and Biodiversity Informatics research group in the Department of Mathematical Sciences, Stellenbosch University, South Africa, from 2018-2020. He was an African-Oxford (AfOx) visiting postdoctoral researcher with the Malaria Atlas Project spatial modelling research group, in the Big Data Institute, Nuffield Department of Medicine, University of Oxford, United Kingdom, in the year 2019. He was also a DAAD-ClimapAfrica postdoctoral research associate with the with the Ecological Modelling and Biodiversity Informatics research group in the Department of Mathematical Sciences, Stellenbosch University, South Africa from 2020 to 2022. His research interests are: Artificial Intelligence, Applied Health Informatics, Computational Modelling in Health, AI in biomedical data science, Applied machine learning, and Bioinformatics (Genomics).