基于深度人工神经网络的死亡证明潜在死因预测模型
A deep artificial neural network based model for underlying cause of death prediction from death certificates
摘要：
从死亡证明中编码潜在的死亡原因是一个现在主要由人类在专家系统（如Iris软件）的潜在帮助下进行的过程。因此，这是一个昂贵的过程，而且可能受到地理空间差异的影响，从而严重损害国际一级死亡统计的可比性。人工智能的最新进展，特别是深度学习方法的兴起，使计算机能够对许多复杂的问题做出有效的决策，这些问题通常被认为是没有人类帮助就无法解决的。然而，它们需要大量的数据来学习，这通常是它们的主要限制因素。然而，CépiDc存储了法国全国范围内的详尽的死亡证明数据库，为机器学习从业者提供了数百万个训练示例。本文介绍了一种基于深度学习的工具，用于从死亡证明中包含的数据中自动编码死亡的根本原因，准确率为97.8%，与Iris软件相比，这是一项重大成就，其在相同的测试示例中评估的准确率为75%。这种改进开辟了一个新的应用领域，从疾病学家一级的批量自动编码到死因统计的国际和时间协调。
英文摘要：
Underlying cause of death coding from death certificates is a process that is nowadays undertaken mostly by humans with a potential assistance from expert systems such as the Iris software. It is as a consequence an expensive process that can in addition suffer from geospatial discrepancies, thus severely impairing the comparability of death statistics at the international level. The recent advances in artificial intelligence, specifically the raise of deep learning methods, has enabled computers to make efficient decisions on a number of complex problem that were typically considered as out of reach without human assistance. They however require a considerable amount of data to learn from, which is typically their main limiting factor. However, the CépiDc stores an exhaustive database of death certificate at the French national scale, amounting to several millions training example available for the machine learning practitioner. This article presents a deep learning based tool for automated coding of the underlying cause of death from the data contained in death certificates with 97.8% accuracy, a substantial achievement compared to the Iris software and its 75% accuracy assessed on the same test examples. Such an improvement opens a whole field of new applications, from nosologist-level batch automated coding to international and temporal harmonization of cause of death statistics.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
