Machine learning models of healthcare expenditures predicting mortality: A cohort study of spousal bereaved Danish individuals

Link to the paper 👉 https://doi.org/10.1371/journal.pone.0289632

Access that quarto-pub link for all the information and the structure of the code in chunks 👇:

https://alkat19.quarto.pub/available_code_mortality_prognosis_expenditures/

The code 👆 illustrates the analysis workflow only and cannot run since the datasets are not allowed to be shared publicly due to their nature.

The current study uses detailed data on individuals. This means that even if direct identifiers like name, date of birth and street address are removed from the data, it is still possible to re-identify the individuals in the study, which would breach basic principles of data protection. Consequently, the data can only be shared under specific conditions. According to Danish law, scientific organizations can be authorized to work with data within Statistics Denmark and can provide access to individual scientists inside and outside of Denmark. Data are available via the Research Service Department at Statistics Denmark: www.dst.dk/da/TilSalg/Forskningsservice for researchers who meet the criteria for access to confidential data.