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# Privacy-preserving generation of a realistic synthetic SNIIRAM database

Thomas Guyet, Marie Guyomard, Raquel Urena

► To cite this version:

Thomas Guyet, Marie Guyomard, Raquel Urena. Privacy-preserving generation of a realistic synthetic SNIIRAM database. PEPR-SanteNum 2025 - Journées Annuelles du PEPR Santé Numérique, Oct 2025, Lille, France, Oct 2025, Lille, France. [hal-05350989](https://hal-INRAA.hal science HAL Id: hal-05350989)

https://inria.hal.science/hal-05350989v1

Submitted on 6 Nov 2025

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Distributed under a Creative Commons Attribution 4.0 International License
# Privacy-preserving generation of a realistic synthetic SNIIRAM database

T. Guyet (AlstroSight/Inria), M. Guyomard (SESSTIM, Univ. Aix-Marseille), R. Urena (SESSTIM, Univ. Aix-Marseille)

## Context and objective

1) **SNIIRAM:** A database used for epidemiological or health economics studies

- Utilized for healthcare organization (DREES, ARS, etc.)

- Key resource for developing innovative health technology solutions

2) Restricted-access health databases: challenging to teach their use or evaluate interest of new data analysis methods

3) **Objective:** **Generate synthetic databases** that are

- Realistic (*)

- Compliant with the database schema of the SNIIRAM

- Privacy-preserving

## Generation principle

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Disease specific
expert knowledge
(scientific articles, medical guidelines)

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Internal patient-centered
representation of
care pathways
+
Healthcare infrastructure

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- population representativeness of patients (age, sex, location), amount of drugs deliveries, procedures, long diseases.

- injected expert care pathway (at individual scales)

- database consistency

## Three different applications of the generator providing sharable databases

### General population synthesis

- evaluation of care distribution reconstruction

- generation of yearly cares of a local population : population (age, sex and location), medics, procedures, hospitalizations.

Purpose: validation of generated distributions

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### Population with stokes (Survival analysis)

- generation of a realistic population with stroke disease (prevalence per age and sex)

- modeling of the death according to a Weibull distribution of the delay (based on C. de Peretti study and data)

- adding background care events as noise

Purpose: used for a course on survival analysis

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### Care pathways for breast cancer in France

- generation of a realistic population with breast cancer treatment

- modeling 10 treatment care pathways

- generating care events based on the distribution of pathways in the population

Purpose: used for benchmarking maching learning models

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<table><caption>Table 2: Distribution of BC nodal status (positive vs. negative) across the simulated profiles. In red we have the fixed proportions obtained from previous simulation steps and in blue the targeted ones (deduced from FRESH study, see SM Section 1.4). Acronyms for BC sub-types: L = Luminal, T = TNBC, H = HER 2+ and U = Undefined.</caption><thead><tr><th rowspan="2"></th><th rowspan="2">BC Type</th><th colspan="2">Nodal Status</th><th rowspan="2">Total</th></tr><tr><th>Positive</th><th>Negative</th></tr></thead><tbody><tr><td colspan="5">Without Chemotherapy</td></tr><tr><td>S</td><td>U</td><td>0.5</td><td>6.5</td><td>7</td></tr><tr><td>S+ET</td><td>L</td><td>0.6</td><td>4.4</td><td>5</td></tr><tr><td>S+RT</td><td>U</td><td>0</td><td>12</td><td>12</td></tr><tr><td>S+RT+ET</td><td>L</td><td>4</td><td>33</td><td>37</td></tr><tr><td colspan="4">Sub-Total</td><td>5.2</td></tr><tr><td colspan="4">Sub-Total</td><td>5.1</td></tr><tr><td colspan="5">With Neoadjuvant Chemotherapy</td></tr><tr><td>CT+S+RT+ET</td><td>L</td><td>0</td><td>3</td><td>3</td></tr><tr><td>CT+S+RT</td><td>T</td><td>0.9</td><td>1.1</td><td>2</td></tr><tr><td>CT+TT+S+RT+ET</td><td>H</td><td>0.8</td><td>1.2</td><td>2</td></tr><tr><td colspan="4">Sub-Total</td><td>1.8</td></tr><tr><td colspan="4">Sub-Total</td><td>1.7</td></tr><tr><td colspan="5">With Adjuvant Chemotherapy</td></tr><tr><td>S+CT+TT+RT+ET</td><td>H</td><td>1.6</td><td>4.4</td><td>6</td></tr><tr><td>S+CT+RT</td><td>T</td><td>1.3</td><td>6.2</td><td>7.5</td></tr><tr><td>S+CT+RT+ET</td><td>L</td><td>9.1</td><td>9.4</td><td>18.5</td></tr><tr><td colspan="4">Sub-Total</td><td>11.8</td></tr><tr><td colspan="4">Sub-Total</td><td>12</td></tr><tr><td colspan="4">Total</td><td>18.8</td></tr><tr><td colspan="4">Total</td><td>18.8</td></tr></tbody></table>

Dumas E, Laot L, Coussy F, Grandal Rejo B, Daoud E, Laas E, Kassara A, Majdling A, Kabirian R, Jochum F, Gougis P, Michel S, Houzard S, Le Bihan-Benjamin C, Bousquet PJ, Hotton J, Azencott CA, Reyal F, Hamy AS. The French Early Breast Cancer Cohort (FRESH): A Resource for the Evaluation of Oncology Practices Based on the French National Healthcare System Research and Evaluations of Oncology Practices Based on the French National Healthcare System Database (SNDS). Cancers (Basel). 2022 May 27;14(11):2671.

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Christine de Peretti, Les risques de décès un an après un accident vasculaire cérébral, Étude et résultats, DREES, 2015

SafePaw

https://gitlab.inria.fr/tguyet/medtrajectory datagen

This work also received government funding managed by the French National Research Agency under France 2030, reference number ANR-22-PESN-0005.

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<footnote>(*) Targeted pieces of realism:</footnote>