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September 10, 2025AlgorithmsOpen Access

Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints

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Results showed strong alignment with real-world data, indicating the effectiveness of the proposed synthetic patient cohort generator.
Validation involved two datasets, NHANES and the Framingham Heart Study, highlighting diverse model performance across correlation scenarios.
The approach employs predefined probability distributions and Cholesky decomposition to reflect real-world correlations effectively.
The proposed model supports reliable simulations in scenarios where access to real-world data is limited, revealing significant advantages over existing methods.

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Implication

Analysis reveals a novel synthetic patient cohort generator effectively preserves correlations and logical constraints, implying enhanced model performance across various datasets.

Cite This Study

Fasseeh et al. (2025) studied this question.

synapsesocial.com/papers/68c1b18554b1d3bfb60e837a https://doi.org/10.3390/a18080475

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Implication

Analysis reveals a novel synthetic patient cohort generator effectively preserves correlations and logical constraints, implying enhanced model performance across various datasets.

Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints

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Implication

Cite This Study

Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints

Key Points

Discussion

Implication

Cite This Study

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