Alice Namale

Achieving high retention of people living with HIV (PLHIV) in care remains a challenge in Uganda, despite substantial progress towards UNAIDS 95–95-95 targets. The purpose of this study was to apply and compare machine learning and deep learning models using de-identified longitudinal clinical data collected from HIV clinics in Uganda to predict clients at high risk of missing treatment appointments. Predictions were made at the visit level, and a missed appointment was defined as failure to attend a scheduled clinical visit within 28 days of the assigned return data in accordance with the PEPFAR definition of loss to follow-up. We compared the performance of traditional machine learning models (i.e. commonly used non-sequential models such as Decision Tree, Random Forest, AdaBoost, and XGBoost) and the Bidirectional Encoder Representations from Transformers (BERT) model, which is designed to model sequential and contextual information in longitudinal data. Feature importance using the Shapley additive exPlanations method was used to identify the most influential predictors. We also evaluated the impact of various sampling techniques, i.e. undersampling, oversampling, and synthetic minority oversampling, to address class imbalance and improve model performance. Model performance was evaluated using accuracy, precision, recall, F1-score, and the Area Under the Curve-Receiver Operating Characteristic (AUC-ROC) metrics. The study was based on a longitudinal dataset of 66,206 PLHIV who initiated HIV care during 2000–2023 in 86 health facilities. The data comprised 1,479,121 clinical visits, an average of 22 clinical visits per client; 158,266 (10.7%) missed appointments, and 49,588 (74.9%) of clients missed at least one appointment. Median (interquartile range [IQR]) age was 38.0 [29.0–47.0] years, and the majority (n = 43,132, 65.1%) were female. The BERT model achieved the highest performance in predicting missed appointments, with an AUC of 0.96 [CI: 0.94–0.97], precision of 89.0% [CI: 87.1%–90.9%], recall of 100% [CI: 100.0%–100.0%], and an F1-score of 94.0% [CI: 92.5%–95.5%] for the missed appointment class. In comparison, the best-performing traditional model, XGBoost with undersampling, achieved an AUC of 0.90 [CI: 0.87–0.92], precision of 33.2% [CI: 30.3%–36.1%], recall of 79.7% [CI: 77.2%–82.2%], and an F1-score of 46.8% [CI: 43.7%–49.9%] for the missed appointment class. Feature importance analysis showed that treatment adherence, visit frequency, treatment duration, and visits on the current regimen were the most influential predictors of appointment interruption. In this retrospective analysis, transformer-based sequential modeling showed promising performance for visit-level prediction of missed HIV appointments compared with the non-sequential models evaluated. These findings should be interpreted cautiously, as further temporal and external validation is required. If validated prospectively, such models may support EMR workflows by identifying clients at higher risk of missed visits, potentially enabling more targeted outreach and retention support in HIV care programs. The online version contains supplementary material available at 10.1186/s12911-026-03434-z.

Background: Achieving high retention of people living with HIV (PLHIV) in care remains a challenge in Uganda, despite substantial progress towards UNAIDS 95-95-95 targets. This study used advanced machine learning and deep learning techniques applied to de-identified longitudinal PLHIV data routinely collected in HIV clinics in Uganda to predict clients at high risk of missing treatment appointments. Methods: We compared the performance of traditional machine learning models (i.e., Decision Tree, Random Forest, AdaBoost, and XGBoost) and the Bidirectional Encoder Representations from Transformers (BERT) model, which is more suitable for analyzing longitudinal data. Feature importance using the Shapley additive exPlanations method was used to identify the most influential predictors. We also evaluated the impact of various sampling techniques, i.e., undersampling, oversampling, and synthetic minority oversampling, to address class imbalance and improve model performance. Model performance was evaluated using accuracy, precision, recall, F1-score, and the Area Under the Curve-Receiver Operating Characteristic (AUC-ROC) metrics. Results: The study was based on a longitudinal dataset of 66,206 PLHIV who initiated HIV care during 2000-2023 in 86 health facilities. The data comprised 1,479,121 clinical visits, an average of 22 clinical visits; 158,266 (10.7%) missed appointments, and 49,588 (74.9%) of clients missed at least one appointment. Median (interquartile range [IQR]) age was 36.0 [29.0 – 47.0] years, and the majority (n=43132, 65%) were female. The BERT model demonstrated superior performance, achieving an AUC score of 0.96, 94.8% accuracy, 97.1% precision, 100% recall, and an F1-score of 94.2%. In comparison, the XGBoost model with undersampling achieved an AUC score of 0.90, 80.7% accuracy, 97.1% precision, 80.8% recall, and an F1-score of 88.2%. Feature importance analysis showed that treatment adherence, visit frequency, treatment duration, and visits on the current regimen were the most influential predictors of appointment interruption. Conclusion: This study highlights the efficacy of transformer-based models like BERT in handling longitudinal clinical data and improving patient retention predictions. Integrating these predictive models into electronic medical systems will facilitate proactive treatment strategies, enabling the identification of clients at risk of disengagement before they miss appointments. This approach may contribute to the improvement of HIV care and support progress towards achieving the HIV program targets in Uganda and potentially elsewhere.

Background: Duplicate patient records pose a significant challenge to healthcare registries and electronic medical record (EMR) systems in Uganda, primarily due to the absence of a national unique patient identifier. These duplicates lead to fragmented patient care, misallocation of resources, and inaccuracies in data reporting, which hinder effective monitoring of disease progression, disrupt continuity of care, and complicate efforts to track patient outcomes. Objective: To evaluate the performance of three classification algorithms in identifying duplicate records of people living with HIV (PLHIV) and to determine a combination of variables that can uniquely identify a PLHIV. Methods: The study used a six-step deduplication process involving dataset extraction, preprocessing, indexing, comparison, classification, and performance evaluation. Records of PLHIV who were active in care between June and November 2022 were extracted from the UgandaEMR system - an EMR installed at 15 public health facilities in six districts in the Rwenzori Region. The dataset included demographic variables, i.e., first name, middle name, last name, sex, age, date of birth, address, and phone number. Three classification algorithms were used to classify the client scores into matches, potential matches, and non-matches, namely i) a threshold-based algorithm, ii) a weighted average score-based algorithm, and iii) a decision tree. Due to the absence of a labeled dataset, the decision tree was trained on data labeled using the two rule-based methods and evaluated on a synthetic reference dataset. Performance of the algorithms was evaluated using sensitivity, specificity, and F-score metrics. Results: A total of 44,717 records for PLHIV active in care in the Rwenzori region from June to November 2022 were extracted. The weighted average score-based algorithm identified 447 (5.8%) records as duplicates and 2996 (10%) as potential duplicates. The threshold-based algorithm identified 118 (0.5%) duplicates and flagged 8560 (21.0%) as potential duplicates. The weighted average score-based algorithm achieved the highest performance: sensitivity (99.0%), specificity (98.8%), and F-score (98.9%); followed by the threshold-based classification: sensitivity (95.3%), specificity (89.1%), and F-score (92.1%); and the decision tree algorithm sensitivity (92.3%), specificity (93.9%, and F-score (93.1%). Conclusions: The weighted average score-based algorithm achieved the best performance. Findings highlight that a combination of a few demographic variables can be employed to differentiate PLHIV. However, improving duplicate record detection at scale will require training these algorithms on a larger dataset that can generalize the PLHIV population in Uganda.

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

Introduction: The COVID-19 pandemic overwhelmed health systems globally and affected the delivery of health services. We conducted a study in Uganda to describe the interventions adopted to maintain the delivery of other health services. Methods: We reviewed documents and interviewed 21 key informants. Thematic analysis was conducted to identify themes using the World Health Organization health system building blocks as a guiding framework. Results: Governance strategies included the establishment of coordination committees and the development and dissemination of guidelines. Infrastructure and commodity strategies included the review of drug supply plans and allowing emergency orders. Workforce strategies included the provision of infection prevention and control equipment, recruitment and provision of incentives. Service delivery modifications included the designation of facilities for COVID-19 management, patient self-management, dispensing drugs for longer periods and the leveraging community patient networks to distribute medicines. However, multi-month drug dispensing led to drug stock-outs while community drug distribution was associated with stigma. Conclusions: Health service maintenance during emergencies requires coordination to harness existing health system investments. The essential services continuity committee coordinated efforts to maintain services and should remain a critical element of emergency response. Self-management and leveraging patient networks should address stigma to support service continuity in similar settings and strengthen service delivery beyond the pandemic.

Introduction: The COVID-19 pandemic overwhelmed health systems globally, and affected delivery of other health services. We conducted a study in Uganda to describe interventions for continuity of access to other health services. Methods: We reviewed documents and interviewed 21 key informants. Thematic analysis was conducted to identify themes using the World Health Organization health system building blocks as a guiding framework. Results: Governance strategies included establishment of coordination committees and development and dissemination of guidelines. Infrastructure and commodities strategies included review of drug supply plans and allowing emergency orders. Workforce strategies included provision of infection prevention and control equipment, recruitment and provision of incentives. Service delivery modifications included designation of facilities for COVID-19 management, patient self-management, dispensing drugs for longer periods and leveraging community patient networks to distribute medicines. However, multi-month drug dispensing led to drug stock-outs while community drug distribution was associated with stigma concerns. Conclusions: Health service maintenance during emergencies requires coordination to harness existing health system investments. The service continuity committee coordinated efforts to maintain services and should remain a critical element of emergency response. Self-management and leveraging patient networks should address stigma to support service continuity in similar settings and strengthen service delivery beyond the pandemic.

Background: Private entities play a major role in health globally. However, their contribution has not been fully optimized to strengthen delivery of public health services. The COVID-19 pandemic has overwhelmed health systems and precipitated coalitions between public and private sectors to address critical gaps in the response. We conducted a study to document the public and private sector partnerships and engagements to inform current and future responses to public health emergencies. Methods: This was a multi-country cross-sectional study conducted in the Democratic Republic of Congo, Nigeria, Senegal and Uganda between November 2020 and March 2021 to assess responses to the COVID-19 pandemic. We conducted a scoping literature review and key informant interviews (KIIs) with private and public health sector stakeholders. The literature reviewed included COVID-19 country guidelines and response plans, program reports and peer-reviewed and non-peer-reviewed publications. KIIs elicited information on country approaches and response strategies specifically the engagement of the private sector in any of the strategic response operations. Results: Across the 4 countries, private sector strengthened laboratory systems, COVID-19 case management, risk communication and health service continuity. In the DRC and Nigeria, private entities supported contact tracing and surveillance activities. Across the 4 countries, the private sector supported expansion of access to COVID-19 testing services through establishing partnerships with the public health sector albeit at unregulated fees. In Senegal and Uganda, governments established partnerships with private sector to manufacture COVID-19 rapid diagnostic tests. The private sector also contributed to treatment and management of COVID-19 cases. In addition, private entities provided personal protective equipment, conducted risk communication to promote adherence to safety procedures and health promotion for health service continuity. However, there were concerns related to reporting, quality and cost of services, calling for quality and price regulation in the provision of services. Conclusions: The private sector contributed to the COVID-19 response through engagement in COVID-19 surveillance and testing, management of COVID-19 cases, and health promotion to maintain health access. There is a need to develop regulatory frameworks for sustainable public–private engagements including regulation of pricing, quality assurance and alignment with national plans and priorities during response to epidemics.