# Analyzing Partitioned FAIR Health Data Responsibly

arXiv: https://arxiv.org/abs/1812.00991 · HTML: https://mlsanity.com/paper/1812.00991/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 2 Dec 2018.

## Built on by (most cited first, 1 of 1)

- [TAPS Responsibility Matrix: A tool for responsible data science by design](https://mlsanity.com/paper/2302.01041/) (2023)

## Abstract (arXiv)

It is widely anticipated that the use of health-related big data will enable further understanding and improvements in human health and wellbeing. Our current project, funded through the Dutch National Research Agenda, aims to explore the relationship between the development of diabetes and socio-economic factors such as lifestyle and health care utilization. The analysis involves combining data from the Maastricht Study (DMS), a prospective clinical study, and data collected by Statistics Netherlands (CBS) as part of its routine operations. However, a wide array of social, legal, technical, and scientific issues hinder the analysis. In this paper, we describe these challenges and our progress towards addressing them.