Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.
Harnessing information from injury narratives in the 'big data' era: understanding and applying machine learning for injury surveillance.
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DOI：
10.1136/injuryprev-2015-041813
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发表时间：
2016-04
期刊：
影响因子：
--
通讯作者：
Smith GS
中科院分区：
文献类型：
--
作者：
Vallmuur K;Marucci-Wellman HR;Taylor JA;Lehto M;Corns HL;Smith GS
每天收集大量的伤害叙述，并以电子方式提供真实的时间，并有很大的潜力，用于伤害监测和评估。已经开发了机器学习算法，以帮助以比依赖于叙述的手动编码时更及时的方式识别病例和分类导致伤害的机制。本文的目的是描述机器学习应用于伤害监测的背景、发展、价值、挑战和未来方向。本文回顾了使用伤害叙述进行机器学习的关键方面，并提供了一个案例研究来演示已建立的人机学习方法的应用。随着时间的推移，随着计算机技术的进步，叙事文本的应用范围和实用性大大增加。存在实际可行的方法用于准确、有效和有意义的伤害叙述的半自动分类。案例研究中描述的人机学习方法实现了高灵敏度和阳性预测值，并将一个大型职业伤害数据库中的人类编码需求减少到不到三分之一。在过去的20年里，伤害监测技术进步的潜力发生了巨大的变化。“大伤害叙述数据”的机器学习为扩展数据源开辟了许多可能性，这些数据源可以提供更全面、持续和及时的监测，为未来的伤害预防政策和实践提供信息。
Vast amounts of injury narratives are collected daily and are available electronically in real time and have great potential for use in injury surveillance and evaluation. Machine learning algorithms have been developed to assist in identifying cases and classifying mechanisms leading to injury in a much timelier manner than is possible when relying on manual coding of narratives. The aim of this paper is to describe the background, growth, value, challenges and future directions of machine learning as applied to injury surveillance. This paper reviews key aspects of machine learning using injury narratives, providing a case study to demonstrate an application to an established human-machine learning approach. The range of applications and utility of narrative text has increased greatly with advancements in computing techniques over time. Practical and feasible methods exist for semi-automatic classification of injury narratives which are accurate, efficient and meaningful. The human-machine learning approach described in the case study achieved high sensitivity and positive predictive value and reduced the need for human coding to less than one-third of cases in one large occupational injury database. The last 20 years have seen a dramatic change in the potential for technological advancements in injury surveillance. Machine learning of ‘big injury narrative data’ opens up many possibilities for expanded sources of data which can provide more comprehensive, ongoing and timely surveillance to inform future injury prevention policy and practice.