publications

An up-to-date list is available on Google Scholar.

2024

1. BMC

   Generalizability of machine learning in predicting antimicrobial resistance in E. coli: a multi-country case study in Africa

   Mike Nsubuga, Ronald Galiwango, Daudi Jjingo, and 1 more author

   BMC Genomics, Mar 2024

   Abs

   Antimicrobial resistance (AMR) remains a significant global health threat particularly impacting low- and middle-income countries (LMICs). These regions often grapple with limited healthcare resources and access to advanced diagnostic tools. Consequently, there is a pressing need for innovative approaches that can enhance AMR surveillance and management. Machine learning (ML) though underutilized in these settings, presents a promising avenue. This study leverages ML models trained on whole-genome sequencing data from England, where such data is more readily available, to predict AMR in E. coli, targeting key antibiotics such as ciprofloxacin, ampicillin, and cefotaxime. A crucial part of our work involved the validation of these models using an independent dataset from Africa, specifically from Uganda, Nigeria, and Tanzania, to ascertain their applicability and effectiveness in LMICs.

2. BMJ

   Virtual reality technology for surgical learning: qualitative outcomes of the first virtual reality training course for emergency and essential surgery delivered by a UK–Uganda partnership

   Helen Please, Karamveer Narang, William Bolton, and 10 more authors

   BMJ Open Quality, Jan 2024

   Abs

   Introduction The extensive resources needed to train surgeons and maintain skill levels in low-income and middle-income countries (LMICs) are limited and confined to urban settings. Surgical education of remote/rural doctors is, therefore, paramount. Virtual reality (VR) has the potential to disseminate surgical knowledge and skill development at low costs. This study presents the outcomes of the first VR-enhanced surgical training course, ‘Global Virtual Reality in Medicine and Surgery’, developed through UK-Ugandan collaborations.Methods A mixed-method approach (survey and semistructured interviews) evaluated the clinical impact and barriers of VR-enhanced training. Course content focused on essential skills relevant to Uganda (general surgery, obstetrics, trauma); delivered through: (1) hands-on cadaveric training in Brighton (scholarships for LMIC doctors) filmed in 360°; (2) virtual training in Kampala (live-stream via low-cost headsets combined with smartphones) and (3) remote virtual training (live-stream via smartphone/laptop/headset).Results High numbers of scholarship applicants (n=130); registrants (Kampala n=80; remote n=1680); and attendees (Kampala n=79; remote n=556, 25 countries), demonstrates widespread appetite for VR-enhanced surgical education. Qualitative analysis identified three key themes: clinical education and skill development limitations in East Africa; the potential of VR to address some of these via 360° visualisation enabling a ‘knowing as seeing’ mechanism; unresolved challenges regarding accessibility and acceptability.Conclusion Outcomes from our first global VR-enhanced essential surgical training course demonstrating dissemination of surgical skills resources in an LMIC context where such opportunities are scarce. The benefits identified included environmental improvements, cross-cultural knowledge sharing, scalability and connectivity. Our process of programme design demonstrates that collaboration across high-income and LMICs is vital to provide locally relevant training. Our data add to growing evidence of extended reality technologies transforming surgery, although several barriers remain. We have successfully demonstrated that VR can be used to upscale postgraduate surgical education, affirming its potential in healthcare capacity building throughout Africa, Europe and beyond.Data may be obtained from a third party and are not publicly available. The participants of this study did not give consent for their data to be shared publicly, so the empirical data are not available due to confidentiality reasons.

2023

1. A machine learning approach to predict E. coli antibacterial resistance using whole-genome sequencing data

   Mike Nsubuga

   Jan 2023

   Accepted: 2024-02-28T08:06:49Z

   Abs

   Background: Antimicrobial resistance (AMR) is a significant global health threat, particularly impacting low- and middle-income countries(LMICS) such as Uganda, where reliable and rapid methods for detecting AMR in E. coli and other pathogens are scarce. This lack can lead to inappropriate treatment and the spread of drug-resistant infections. This thesis undertakes a comprehensive study, where various machine learning models to predict AMR in E. coli for ciprofloxacin(CIP), ampicillin(AMP), and cefotaxime(CTX) were trained on whole genome sequencing (WGS) data from England where such data is more readily available. A separate Ugandan dataset was used for validation purposes, further demonstrating the generalizability and effectiveness of the models in LMICS. Methods: 1496 (CIP), 1428 (CTX), and 1396 (AMP) sequences from England were divided into training and testing. 42 from Uganda were used for validation. Eight different machine learning models were trained and tested: Logistic Regression(LR), Random Forest(RF), Gradient Boosting(GB), XGBoost(XGB), LightGBM(LGBM), CatBoost(CB), Feed-Forward Neural Network(FFNN), and Support Vector Machine(SVM). The models were evaluated based on precision, recall, and Area Under the Receiver Operating Characteristic Curve (AUC-ROC). Upsampling techniques were implemented to address class imbalance in the data. Results: Model predictive performance varied significantly across different antibiotics, underlining the critical role of model selection and dataset characteristics. Notably, the FFNN model demonstrated superior performance during testing for CIP (accuracy 84%; F1 0.55; AUC 91%), LR for CTX (accuracy 91%; F1 0.37; AUC 83%) and GB for AMP (accuracy 57%; F1 0.62, AUC 53%), while the LGBM and RF models outperformed others in same scenarios (p \textless 0.001). Upsampling did not significantly improve the models’ performance, underscoring the complexity and high-dimensionality of SNP data. Despite high accuracy scores with the Ugandan validation dataset(FFNN with CIP accuracy 95%, LR with AMP accuracy 98% and GB with CTX accuracy 65%), the models struggled with the recall metric due to severe class imbalance. Key mutations associated with antimicrobial resistance were identified for these antibiotics. Conclusion: As the threat of AMR continues to rise, the successful application of these models - particularly on the Ugandan dataset, signals a promising avenue for improving AMR detection and treatment strategies in LMICS were genomic data is scarce. This work thus not only expands our current understanding of the genetic underpinnings of AMR but also provides a robust methodological framework that can guide future research and applications in the fight against antimicrobial resistance.

2022

1. BMC

   Feasibility of virtual reality based training for optimising COVID-19 case handling in Uganda

   Paul Buyego, Elizabeth Katwesigye, Grace Kebirungi, and 9 more authors

   BMC Medical Education, Apr 2022

   Abs

   Epidemics and pandemics are causing high morbidity and mortality on a still-evolving scale exemplified by the COVID-19 pandemic. Infection prevention and control (IPC) training for frontline health workers is thus essential. However, classroom or hospital ward-based training portends an infection risk due to the in-person interaction of participants. We explored the use of Virtual Reality (VR) simulations for frontline health worker training since it trains participants without exposing them to infections that would arise from in-person training. It does away with the requirement for expensive personal protective equipment (PPE) that has been in acute shortage and improves learning, retention, and recall. This represents the first attempt in deploying VR-based pedagogy in a Ugandan medical education context.