Trang: 38
Tập 34, số 8 2024
Forecasting HIV Treatment loss of follow up rate Using Deep Learning and Survival Analysis in Ho Chi Minh City
Tác giả: Nguyen Van Duong, Pham Thanh Dat, Tran Tan Thanh, Le Ngoc Bao Ngan, Nguyen Vu Minh
Duy, Nguyen Thi Hai Van
Tóm tắt:
Maintaining antiretroviral therapy (ART) is critical for improving treatment outcomes and preventing the
transmission of HIV. However, dropout rates remain a significant challenge, particularly in urban centers like Ho
Chi Minh City, which has the largest population of ART patients in Vietnam. This study aimed to predict ART
dropout rates using deep learning methods, specifically a one-dimensional convolutional neural network (1D CNN),
in combination with survival analysis to identify factors affecting treatment retention. This approach leverages
advanced machine learning techniques to analyze time-to-event data, offering deeper insights into patient retention
dynamics. The results demonstrate that the 1D CNN model significantly outperformed traditional methods, achieving
an F1-score of 0.847 and a ROC AUC of 0.890, compared to significantly lower scores form traditional models. This
highlights the model’s ability to efficiently process complex, high-dimensional datasets and identify high-risk patients
for timely intervention. The model’s findings indicate retention rates of 52.09% after one year and 27.74% after 1.5
years, with factors such as treatment enrollment type and the presence of comorbidities being identified as significant
factors reducing dropout rates. We recommend comprehensive care for patients at treatment facilities as a strategy to
retain them in ART services, emphasizing the importance of addressing not only HIV treatment but also coexisting
health conditions to enhance overall retention.
Summary:
Từ khóa:
HIV; Survival Analysis; CNN
DOI: https://doi.org/10.51403/0868-2836/2024/2142
File nội dung:
bai-4.pdf
Tải file:
