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arXiv · 1908.09712

A deep artificial neural network based model for underlying cause of death prediction from death certificates

Louis Falissard·Claire Morgand·Sylvie Roussel·Claire Imbaud·Walid Ghosn·Karim Bounebache·Grégoire Rey
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Abstract

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.

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Louis Falissard, Claire Morgand, Sylvie Roussel, Claire Imbaud, Walid Ghosn, Karim Bounebache, Grégoire Rey. 2019-08-26. A deep artificial neural network based model for underlying cause of death prediction from death certificates. https://arxiv.org/abs/1908.09712

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