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

TL;DR

This paper introduces a deep learning model that automates the coding of underlying causes of death from death certificates, achieving significantly higher accuracy than traditional software and enabling better international health data comparison.

Contribution

The paper presents a novel deep neural network approach for cause of death prediction, utilizing a large French death certificate database to improve accuracy over existing methods.

Findings

Achieved 97.8% accuracy in cause of death prediction

Outperformed Iris software with 75% accuracy on the same dataset

Enabled potential for automated, harmonized international death statistics

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 C\'epiDc 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.