Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2024 Jul 15;19(1):265.
doi: 10.1186/s13023-024-03254-2.

Synthetic datasets for open software development in rare disease research

Ibraheem Al-Dhamari  1   2 , Hammam Abu Attieh  3 , Fabian Prasser  3
Affiliations

Synthetic datasets for open software development in rare disease research

Ibraheem Al-Dhamari et al. Orphanet J Rare Dis. .

Abstract

Background: Globally, researchers are working on projects aiming to enhance the availability of data for rare disease research. While data sharing remains critical, developing suitable methods is challenging due to the specific sensitivity and uniqueness of rare disease data. This creates a dilemma, as there is a lack of both methods and necessary data to create appropriate approaches initially. This work contributes to bridging this gap by providing synthetic datasets that can form the foundation for such developments.

Methods: Using a hierarchical data generation approach parameterised with publicly available statistics, we generated datasets reflecting a random sample of rare disease patients from the United States (US) population. General demographics were obtained from the US Census Bureau, while information on disease prevalence, initial diagnosis, survival rates as well as race and sex ratios were obtained from the information provided by the US Centers for Disease Control and Prevention as well as the scientific literature. The software, which we have named SynthMD, was implemented in Python as open source using libraries such as Faker for generating individual data points.

Results: We generated three datasets focusing on three specific rare diseases with broad impact on US citizens, as well as differences in affected genders and racial groups: Sickle Cell Disease, Cystic Fibrosis, and Duchenne Muscular Dystrophy. We present the statistics used to generate the datasets and study the statistical properties of output data. The datasets, as well as the code used to generate them, are available as Open Data and Open Source Software.

Conclusion: The results of our work can serve as a starting point for researchers and developers working on methods and platforms that aim to improve the availability of rare disease data. Potential applications include using the datasets for testing purposes during the implementation of information systems or tailored privacy-enhancing technologies.

Keywords: Development; Evaluation; Rare diseases; Statistics; Synthetic data.

PubMed Disclaimer

Conflict of interest statement

The authors declare that they have no competing interests.

Figures

Fig. 1
Fig. 1
Charts illustrating the basic demographic data collected about the US population (year: 2020): a Population per state, b Population per age is categorized by different age groups for simplicity
Fig. 2
Fig. 2
Overview of the synthetic data generation process and the statistics used
None
Algorithm 1 Data generation algorithm
See this image and copyright information in PMC

References

    1. United States Congress. Rare Diseases Act of 2002. 2002. Public Law No. 107-280. https://www.govinfo.gov/content/pkg/PLAW-107publ280/pdf/PLAW-107publ280.pdf. Accessed 8 July 2024.
    1. European Commission. Regulation (EC) No 141/2000 of the European Parliament and of the Council. Off J Eur Communities. 2000. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX:32000R0141. Accessed 8 July 2024.
    1. Wästfelt M, Fadeel B, Henter JI. A journey of hope: lessons learned from studies on rare diseases and orphan drugs. J Intern Med. 2006;260(1):1–10. doi: 10.1111/j.1365-2796.2006.01666.x. - DOI - PubMed
    1. Peña-Guerrero J, Nguewa PA, García-Sosa AT. Machine learning, artificial intelligence, and data science breaking into drug design and neglected diseases. WIREs Comput Mol Sci. 2021;11(5):e1513. doi: 10.1002/wcms.1513. - DOI
    1. MacLeod H, Abbott J, Patil S. Small data privacy protection: An exploration of the utility of anonymized data of people with rare diseases. In: Proceedings of the 2017 Workshop on Interactive Systems in Healthcare (WISH’17). New York: Association for Computing Machinery; 2017.

LinkOut - more resources