Using Machine Learning to Detect Fraudulent SMSs in Chichewa

Amelia Taylor, Amoss Robert Using Machine Learning to Detect Fraudulent SMSs in Chichewa. arXiv:2502.16947. https://doi.org/10.48550/arXiv.2502.16947

SMS enabled fraud is of great concern globally. Building classifiers based on machine learning for SMS fraud requires the use of suitable datasets for model training and validation. Most research has centred on the use of datasets of SMSs in English. This paper introduces a first dataset for SMS fraud detection in Chichewa, a major language in Africa, and reports on experiments with machine learning algorithms for classifying SMSs in Chichewa as fraud or non-fraud. Read more

Feb 24, 2025
sms-fraud-certificate
Assessing language barriers in health facilities in Malawi

Taylor, A., Kazembe, P. Assessing language barriers in health facilities in Malawi. BMC Health Serv Res 24, 1393 (2024). https://doi.org/10.1186/s12913-024-11901-4

Language barriers in healthcare lead to miscommunication between professionals and patients, thereby reducing the quality of and equitable access to healthcare. In African countries, the recognition and formal study of these barriers is severely limited despite Africa having more languages than any other continent. Read more

Nov 13, 2024
Self-Directed Learning for Community Health Workers in Malawi Through Generative AI

A. Taylor, M. Magwira, C. Chamangwana, E. Chapuma, T. Liwewe and C. Kankhwali. Self-Directed Learning for Community Health Workers in Malawi Through Generative AI. 2024 IEEE 12th International Conference on Healthcare Informatics (ICHI), Orlando, FL, USA, 2024, pp. 574-579. doi: 10.1109/ICHI61247.2024.00092

In many lower and middle-income countries, a lack of resources affects the availability and quality of education and training. In the healthcare domain, access to knowledge can make the difference between life and death. Timely access to technical and clinical guidelines to support decisions is crucial. Read more

Aug 22, 2024
Insights into COVID-19 data collection and management in Malawi: exploring processes, perceptions, and data discrepancies

Taylor A, Liwewe T, Todd J et al. Insights into COVID-19 data collection and management in Malawi: exploring processes, perceptions, and data discrepancies. [version 1; peer review: 1 approved, 2 approved with reservations]. Wellcome Open Res 2024, 9:217. doi: 10.12688/wellcomeopenres.21131.1

The completion of case-based surveillance forms was vital for case identification during COVID-19 surveillance in Malawi. Despite significant efforts, the resulting national data suffered from gaps and inconsistencies which affected its optimal usability. The objectives of this study were to investigate the processes Read more

Aug 22, 2024
Lessons and Applications from utilising OMOP to Malawi Data.

Dr Amelia Taylor, Moses Gwaza INSPIRE PEACH Dissemination Workshop.

Jun 26-27, 2023
Insights into COVID-19 Data Collection and Management in Malawi.

Dr Amelia Taylor, Dr. Thokozani Liwewe INSPIRE PEACH Dissemination Workshop.

Jun 26-27, 2023
AI for Organisations in Malawi.

Dr Amelia Taylor. The Pan African Big Data, Analytics and AI 3rd Annual Conference.

April 26-27, 2023
Using OHDSI for health informatics in Malawi.

Dr Amelia Taylor, Gonjetso Chinyama, Moses Gwaza. The Pan African Big Data, Analytics and AI 3rd Annual Conference.

April 26-27, 2023
Can AI Have Its Cake and Eat It? Reducing Bias in AI Models May Not Always Be Desirable.

Dr Amelia Taylor. Blog Post on Gender at Work

May 23, 2023