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MasterThesis

Master Thesis

https://github.com/laurajochim/MasterThesis

Science Score: 44.0%

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    Found CITATION.cff file
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    Low similarity (4.7%) to scientific vocabulary
Last synced: 10 months ago · JSON representation ·

Repository

Master Thesis

Basic Info
  • Host: GitHub
  • Owner: laurajochim
  • License: gpl-3.0
  • Language: R
  • Default Branch: main
  • Homepage:
  • Size: 36.8 MB
Statistics
  • Stars: 0
  • Watchers: 1
  • Forks: 0
  • Open Issues: 0
  • Releases: 1
Created about 1 year ago · Last pushed about 1 year ago
Metadata Files
Readme License Citation

README.md

Methodology for generating and evaluating realistic synthetic healthcare data

This repository contains the code, data, and documentation for my Master's Thesis on generating and evaluating realistic synthetic healthcare datasets. It demonstrates a reproducible pipeline for:

Data Cleaning — Preparing clinical (EHR) and proteomic (TCGA) datasets.

Synthetic Data Generation — Creating synthetic patient records using multiple methods (synthpop, vine copula, ctGAN).

Evaluation — Comparing univariate distributions and other metrics between real and synthetic data.

The was approved by the ethics committee of the faculty of social and Behavioural Sciences of the University of Utrecht:

FETC: 24-2032

This archive can be accessed via GitHub for an unlimited amount of time. I am responsible for the research archive. If there are any questions, feel free to contact me via: l.jochim@students.uu.nl

Owner

  • Login: laurajochim
  • Kind: user
  • Location: Netherlands

Citation (CITATION.cff) 

cff-version: 1.1.0
message: "If you use this software, please cite it as below."
authors:
- family-names: Jochim
  given-names: Laura
orcid: https://orcid.org/0009-0001-4577-4152
title:laurajochim/Methodology-for-generating-and-evaluating-realistic-synthetic-healthcare-data: First thesis release
version: v0.1.0
date-released: 2025-05-10

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Dependencies

environment.yml pypi
  • absl-py ==2.1.0
  • astunparse ==1.6.3
  • boto3 ==1.37.18
  • botocore ==1.37.18
  • contourpy ==1.3.1
  • cycler ==0.12.1
  • faker ==35.2.0
  • flatbuffers ==25.1.24
  • fonttools ==4.56.0
  • fsspec ==2024.12.0
  • gast ==0.6.0
  • google-pasta ==0.2.0
  • grpcio ==1.70.0
  • h5py ==3.12.1
  • keras ==3.8.0
  • kiwisolver ==1.4.8
  • libclang ==18.1.1
  • markdown ==3.7
  • markdown-it-py ==3.0.0
  • matplotlib ==3.10.0
  • mdurl ==0.1.2
  • ml-dtypes ==0.4.1
  • namex ==0.0.8
  • narwhals ==1.26.0
  • numpy ==2.2.4
  • opt-einsum ==3.4.0
  • optree ==0.14.0
  • plotly ==6.0.0
  • protobuf ==5.29.3
  • pyparsing ==3.2.1
  • rich ==13.9.4
  • seaborn ==0.13.2
  • sympy ==1.13.1
  • tensorboard ==2.18.0
  • tensorboard-data-server ==0.7.2
  • termcolor ==2.5.0
  • threadpoolctl ==3.5.0
  • tzdata ==2025.1
  • werkzeug ==3.1.3
  • wrapt ==1.17.2