{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T17:33:39Z","timestamp":1772300019031,"version":"3.50.1"},"reference-count":25,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2025,8,1]],"date-time":"2025-08-01T00:00:00Z","timestamp":1754006400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Algorithms"],"abstract":"<jats:p>Large, high-quality patient datasets are essential for applications like economic modeling and patient simulation. However, real-world data is often inaccessible or incomplete. Synthetic patient data offers an alternative, and current methods often fail to preserve clinical plausibility, real-world correlations, and logical consistency. This study presents a patient cohort generator designed to produce realistic, statistically valid synthetic datasets. The generator uses predefined probability distributions and Cholesky decomposition to reflect real-world correlations. A dependency matrix handles variable relationships in the right order. Hard limits block unrealistic values, and binary variables are set using percentiles to match expected rates. Validation used two datasets, NHANES (2021\u20132023) and the Framingham Heart Study, evaluating cohort diversity (general, cardiac, low-dimensional), data sparsity (five correlation scenarios), and model performance (MSE, RMSE, R2, SSE, correlation plots). Results demonstrated strong alignment with real-world data in central tendency, dispersion, and correlation structures. Scenario A (empirical correlations) performed best (R2 = 86.8\u201399.6%, lowest SSE and MAE). Scenario B (physician-estimated correlations) also performed well, especially in a low-dimensions population (R2 = 80.7%). Scenario E (no correlation) performed worst. Overall, the proposed model provides a scalable, customizable solution for generating synthetic patient cohorts, supporting reliable simulations and research when real-world data is limited. While deep learning approaches have been proposed for this task, they require access to large-scale real datasets and offer limited control over statistical dependencies or clinical logic. Our approach addresses this gap.<\/jats:p>","DOI":"10.3390\/a18080475","type":"journal-article","created":{"date-parts":[[2025,8,4]],"date-time":"2025-08-04T16:41:54Z","timestamp":1754325714000},"page":"475","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Generating Realistic Synthetic Patient Cohorts: Enforcing Statistical Distributions, Correlations, and Logical Constraints"],"prefix":"10.3390","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9878-7734","authenticated-orcid":false,"given":"Ahmad Nader","family":"Fasseeh","sequence":"first","affiliation":[{"name":"Syreon Middle East, Alexandria 5424041, Egypt"},{"name":"Faculty of Pharmacy, Alexandria University, Alexandria 5424041, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2689-5968","authenticated-orcid":false,"given":"Rasha","family":"Ashmawy","sequence":"additional","affiliation":[{"name":"Syreon Middle East, Alexandria 5424041, Egypt"},{"name":"Clinical Research Department, Maamora Chest Hospital, Alexandria 5424041, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Rok","family":"Hren","sequence":"additional","affiliation":[{"name":"Syreon Research Institute, 1142 Budapest, Hungary"},{"name":"Institute of Mathematics, Physics, and Mechanics, 1000 Ljubljana, Slovenia"},{"name":"Faculty of Mathematics and Physics, University of Ljubljana, 1000 Ljubljana, Slovenia"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kareem","family":"ElFass","sequence":"additional","affiliation":[{"name":"Syreon Middle East, Alexandria 5424041, Egypt"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0009-0004-0110-8395","authenticated-orcid":false,"given":"Attila","family":"Imre","sequence":"additional","affiliation":[{"name":"Syreon Research Institute, 1142 Budapest, Hungary"},{"name":"Center for Health Technology Assessment, Semmelweis University, 1085 Budapest, Hungary"},{"name":"Center for Pharmacology and Drug Research & Development, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-6513-5147","authenticated-orcid":false,"given":"Bertalan","family":"N\u00e9meth","sequence":"additional","affiliation":[{"name":"Syreon Research Institute, 1142 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"D\u00e1vid","family":"Nagy","sequence":"additional","affiliation":[{"name":"Syreon Research Institute, 1142 Budapest, Hungary"},{"name":"Center for Health Technology Assessment, Semmelweis University, 1085 Budapest, Hungary"},{"name":"Center for Pharmacology and Drug Research & Development, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Bal\u00e1zs","family":"Nagy","sequence":"additional","affiliation":[{"name":"Syreon Research Institute, 1142 Budapest, Hungary"},{"name":"Center for Health Technology Assessment, Semmelweis University, 1085 Budapest, Hungary"},{"name":"Center for Pharmacology and Drug Research & Development, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1004-1848","authenticated-orcid":false,"given":"Zolt\u00e1n","family":"Vok\u00f3","sequence":"additional","affiliation":[{"name":"Syreon Research Institute, 1142 Budapest, Hungary"},{"name":"Center for Health Technology Assessment, Semmelweis University, 1085 Budapest, Hungary"},{"name":"Center for Pharmacology and Drug Research & Development, Semmelweis University, 1085 Budapest, Hungary"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2025,8,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Gonzales, A., Guruswamy, G., and Smith, S.R. 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