000			01754cam a2200181 4500500
005			20250413011241.0
041			_afre
042			_adc
100	1	0	_aEchajari, Loubna _eauthor
700	1	0	_a Jeanningros, Hugo _eauthor
700	1	0	_a Lewkowicz, Myriam _eauthor
245	0	0	_aProduction of mortality data. The renewal of coding procedures based on deep learning
260			_c2025.
500			_a82
520			_aCause-of-mortality statistics are one of the oldest medical statistics available. Mortality data, which provide essential information for general knowledge on a population’s health, and are a tool for international comparisons, are also a growing challenge for the governance of public health, particularly in crisis situations. Based on a study of documents from the bodies that regulate and produce these data, the article examines the socio-technical trajectory of cause-of-mortality coding and analyses the context in which a deep-learning method was used during the COVID period. From a theoretical perspective combining the sociology of science and technology with the sociology of quantification, it interprets the integration of connectionist AI methodologies into the coding process as a further stage in the technical trajectory of the tool that began with automation. It also sheds light on the institutional context of the use of neural networks and the consolidation of that use, relating them to the recent functions of mortality statistics in the governance of health crises and issues around the availability and speed of their production.
786	0		_nRéseaux | o 248 | 6 | 2025-01-14 | p. 193-226 | 0751-7971
856	4	1	_uhttps://shs.cairn.info/journal-reseaux-2024-6-page-193?lang=en&redirect-ssocas=7080
999			_c1103044 _d1103044