Department of Population and Quantitative Health Sciences, School of Medicine, Case Western Reserve University, Cleveland, OH, USA.
Les Centres Haitian Group for the Study of Kaposi's Sarcoma and Opportunistic Infections (GHESKIO), Port-au-Prince, Haiti.
Int J Health Geogr. 2021 Jan 25;20(1):5. doi: 10.1186/s12942-021-00259-z.
The health burden in developing world informal settlements often coincides with a lack of spatial data that could be used to guide intervention strategies. Spatial video (SV) has proven to be a useful tool to collect environmental and social data at a granular scale, though the effort required to turn these spatially encoded video frames into maps limits sustainability and scalability. In this paper we explore the use of convolution neural networks (CNN) to solve this problem by automatically identifying disease related environmental risks in a series of SV collected from Haiti. Our objective is to determine the potential of machine learning in health risk mapping for these environments by assessing the challenges faced in adequately training the required classification models.
We show that SV can be a suitable source for automatically identifying and extracting health risk features using machine learning. While well-defined objects such as drains, buckets, tires and animals can be efficiently classified, more amorphous masses such as trash or standing water are difficult to classify. Our results further show that variations in the number of image frames selected, the image resolution, and combinations of these can be used to improve the overall model performance.
Machine learning in combination with spatial video can be used to automatically identify environmental risks associated with common health problems in informal settlements, though there are likely to be variations in the type of data needed for training based on location. Success based on the risk type being identified are also likely to vary geographically. However, we are confident in identifying a series of best practices for data collection, model training and performance in these settings. We also discuss the next step of testing these findings in other environments, and how adding in the simultaneously collected geographic data could be used to create an automatic health risk mapping tool.
发展中国家非正规住区的健康负担往往与缺乏空间数据相吻合，而这些空间数据本可以用来指导干预策略。空间视频 (SV) 已被证明是一种收集环境和社会数据的有用工具，可以在细粒度上进行，尽管将这些空间编码的视频帧转换为地图所需的工作限制了可持续性和可扩展性。在本文中，我们探讨了使用卷积神经网络 (CNN) 通过自动识别海地 SV 系列中与疾病相关的环境风险来解决这个问题。我们的目标是通过评估充分训练所需分类模型所面临的挑战，来确定机器学习在这些环境中的健康风险测绘中的潜力。
我们表明，SV 可以成为一种合适的来源，通过机器学习自动识别和提取健康风险特征。虽然可以有效地对排水渠、水桶、轮胎和动物等定义明确的物体进行分类，但对垃圾或积水等更无定形的物体进行分类则较为困难。我们的结果还表明，可以使用选择的图像帧数、图像分辨率以及这些的组合的变化来提高整体模型性能。
机器学习与空间视频相结合可用于自动识别非正规住区常见健康问题相关的环境风险，尽管基于位置，训练所需的数据类型可能存在差异。基于所识别的风险类型的成功也可能存在地域差异。但是，我们有信心在这些环境中确定一系列最佳的数据收集、模型培训和性能实践。我们还讨论了在其他环境中测试这些发现以及如何添加同时收集的地理数据以创建自动健康风险测绘工具的下一步。
