This paper is in the following e-collection/theme issue:
Natural Language Processing (767) Advanced Data Analytics in eHealth (155) Big Data (273) Tools, Programs and Algorithms (307) Clinical Informatics (1112) Data Science (296) JMIR Theme Issue: COVID-19 Special Issue (2479) Public (e)Health, Digital Epidemiology and Public Health Informatics (833) Artificial Intelligence, Machine Learning, and Natural Language Processing for Public Health (41)Published on in Vol 12 (2024)
Preprints (earlier versions) of this paper are
available at
https://preprints.jmir.org/preprint/54246, first published
.

A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study
A New Natural Language Processing–Inspired Methodology (Detection, Initial Characterization, and Semantic Characterization) to Investigate Temporal Shifts (Drifts) in Health Care Data: Quantitative Study
Authors of this article:
Bruno Paiva1






































There are no citations yet available for this article according to Crossref .