Nyarko Eric, Agyemang Edmund Fosu, Ameho Ebenezer Kwesi, Agyekum Louis, Gutiérrez José María, Fernandez Eduardo Alberto
Department of Statistics and Actuarial Science, College of Basic and Applied Sciences, University of Ghana, Legon, Accra, Ghana.
School of Mathematical and Statistical Science, College of Sciences, University of Texas Rio Grande Valley, Edinburg, Texas, United States of America.
PLoS Negl Trop Dis. 2024 Dec 13;18(12):e0012736. doi: 10.1371/journal.pntd.0012736. eCollection 2024 Dec.
Snakebite envenoming is a serious condition that affects 2.5 million people and causes 81,000-138,000 deaths every year, particularly in tropical and subtropical regions. The World Health Organization has set a goal to halve the deaths and disabilities related to snakebite envenoming by 2030. However, significant challenges in achieving this goal include a lack of robust research evidence related to snakebite incidence and treatment, particularly in sub-Saharan Africa. This study aimed to combine established methodologies with the latest tools in Artificial Intelligence to assess the barriers to effective snakebite treatment in Ghana.
We used a MaxDiff statistical experiment design to collect data, and six supervised machine learning models were applied to predict responses whose performance showed an advantage over the other through 6921 data points partitioned using the hold-back validation method, with 70% training and 30% validation. The results were compared using key metrics: Akaike Information Criterion corrected, Bayesian Information Criterion, Root Average Squared Error, and Fit Time in milliseconds.
Considering all the responses, none of the six machine learning algorithms proved superior, but the Generalized Regression Model (Ridge) performed consistently better among the candidate models. The model consistently predicted several key significant barriers to effective snakebite treatment, such as the high cost of antivenoms, increased use of unorthodox, harmful practices, lack of access to effective antivenoms in remote areas when needed, and resorting to unorthodox and harmful practices in addition to hospital treatment.
The combination of a MaxDiff statistical experiment design to collect data and six machine learning models allowed the identification of barriers to accessing effective therapies for snakebite envenoming in Ghana. Addressing these barriers through targeted policy interventions, including intensified advocacy, continuous education, community engagement, healthcare worker training, and strategic investments, can enhance the effectiveness of snakebite treatment, ultimately benefiting snakebite victims and reducing the burden of snakebite envenoming. There is a need for robust regulatory frameworks and increased antivenom production to address these barriers.
蛇咬伤中毒是一种严重疾病，每年影响250万人，并导致81000至138000人死亡，特别是在热带和亚热带地区。世界卫生组织设定了到2030年将与蛇咬伤中毒相关的死亡和残疾人数减半的目标。然而，实现这一目标面临的重大挑战包括缺乏与蛇咬伤发病率和治疗相关的有力研究证据，特别是在撒哈拉以南非洲地区。本研究旨在将既定方法与人工智能最新工具相结合，以评估加纳有效治疗蛇咬伤的障碍。
我们使用最大差异统计实验设计来收集数据，并应用六种监督式机器学习模型来预测反应，通过使用保留验证方法划分的6921个数据点，其性能显示出优于其他模型，其中70%用于训练，30%用于验证。使用关键指标对结果进行比较：校正后的赤池信息准则、贝叶斯信息准则、均方根误差和以毫秒为单位的拟合时间。
考虑所有反应，六种机器学习算法均未证明具有优越性，但广义回归模型（岭回归）在候选模型中表现始终更好。该模型始终预测出有效治疗蛇咬伤的几个关键重大障碍，例如抗蛇毒血清成本高昂、非正统有害做法的使用增加、偏远地区在需要时无法获得有效抗蛇毒血清，以及除医院治疗外还采用非正统有害做法。
结合最大差异统计实验设计来收集数据和六种机器学习模型，能够识别加纳蛇咬伤中毒有效治疗的获取障碍。通过有针对性的政策干预来解决这些障碍，包括加强宣传、持续教育、社区参与、医护人员培训和战略投资，可以提高蛇咬伤治疗的有效性，最终使蛇咬伤受害者受益并减轻蛇咬伤中毒的负担。需要强有力的监管框架和增加抗蛇毒血清产量来解决这些障碍。
