论文标题
在被忽视的热带疾病中应用机器学习的进展和挑战
Progress and Challenges for the Application of Machine Learning for Neglected Tropical Diseases
论文作者
论文摘要
被忽视的热带疾病（NTD）继续影响东南亚和西太平洋地区国家个人的生计。这些疾病长期以来一直存在，并引起了毁灭性的健康问题，并使低收入国家（发展中国家）的人们造成了毁灭性的健康问题和经济下降。估计每年有17亿人口遭受一个或多个NTD，这使大约五分之一的人面临着NTD的风险。除了健康和社会影响外，NTD还为患者，亲戚造成了巨大的财务负担，并将造成数十亿美元的收入，仅降低了发展中国家的劳动生产率。迫切需要更好地改善对NTD的控制和消除或消除努力。这可以通过利用机器学习工具来改善监视，预测和检测计划，并通过发现针对这些病原体的新疗法来打击NTD。这篇评论调查了机器学习工具在NTD中的当前应用，以及提高NTDS监视，管理和治疗的最新面临的挑战。
Neglected tropical diseases (NTDs) continue to affect the livelihood of individuals in countries in the Southeast Asia and Western Pacific region. These diseases have been long existing and have caused devastating health problems and economic decline to people in low- and middle-income (developing) countries. An estimated 1.7 billion of the world's population suffer one or more NTDs annually, this puts approximately one in five individuals at risk for NTDs. In addition to health and social impact, NTDs inflict significant financial burden to patients, close relatives, and are responsible for billions of dollars lost in revenue from reduced labor productivity in developing countries alone. There is an urgent need to better improve the control and eradication or elimination efforts towards NTDs. This can be achieved by utilizing machine learning tools to better the surveillance, prediction and detection program, and combat NTDs through the discovery of new therapeutics against these pathogens. This review surveys the current application of machine learning tools for NTDs and the challenges to elevate the state-of-the-art of NTDs surveillance, management, and treatment.
