These datasets represent fully synthetic, privacy-free populations that emulate the structure and statistical behavior of modern hospital data as of October 2025.

Each population includes four inter-linked tables describing demographics, admissions, diagnoses, and laboratory measurements.

No real patient information is used or referenced anywhere — every record is computer-generated, de-identified by design, and derived entirely from algorithmic logic.

The populations reproduce the diversity of a contemporary healthcare system (≈ 30 – 95 years old in 2025) with realistic comorbidity networks, laboratory correlations, and healthcare-use patterns that collectively behave like authentic EMRs.

Comparison: 2015 EMRBots vs. October 2025 Clinically-Informed Synthetic EMR

Feature	EMRBots (2015)	Clinically-Informed Synthetic Population (October 2025)
Data Source	Randomized distributions seeded manually	Statistically generated using clinical logic and physiologic dependencies
Conditions	ICD-coded, random frequency	15 chronic conditions with realistic co-occurrence networks and age trends
Admissions	Random encounters	Admissions scale with severity index and comorbidity load
Laboratory Data	Independent random values	30 + labs with coupled physiologic equations (Hct≈3×Hgb, AG = Na−(Cl+CO₂), TP = Alb+Glob)
Clinical Correlations	None	Condition-specific lab shifts (↑ Glucose → Diabetes; ↑ BUN/Cr → CKD)
Severity Measure	None	Transparent “Severity Index” = count of chronic conditions
Comorbidity Links	Independent diseases	Realistic clusters – metabolic, cardiac, pulmonary, neuro-aging, psychosomatic
Validation	Visual inspection only	Automated analytics, co-occurrence maps, and physiologic consistency tests
Clinical Plausibility	Moderate (for demonstration)	High – patterns mirror true hospital data while remaining 100 % synthetic
Privacy Risk	None (toy data)	None – no real patients, no protected health information
Intended Use	Conceptual teaching	ML training, RWE validation, EHR pipeline testing, education, benchmarking

Summary

The October 2025 datasets represent the next generation of synthetic EMR design — clinically coherent, statistically stable, and physiologically accurate.

They bridge the gap between early random EMR simulators and research-grade synthetic populations that preserve medical realism without privacy concerns.

Table Structure

Table	Description
PatientCorePopulatedTable.txt	One record per synthetic patient, including demographics, behavior, and a severity index (number of chronic conditions).
AdmissionsCorePopulatedTable.txt	One record per simulated hospital stay with realistic start/end dates linked to each patient.
AdmissionsDiagnosesCorePopulatedTable.txt	Chronic-condition descriptors attached to each admission (15 canonical diseases).
LabsCorePopulatedTable.txt	Time-stamped laboratory values across 30 + analytes, each obeying physiologic constraints and disease-specific drifts.

Why the October 2025 Synthetic EMR Data Are High Quality

These datasets are entirely artificial yet clinically logical.
Their quality derives from coordinated realism across demographics, disease architecture, laboratory dependencies, and severity-linked behavior.

Demographic and Behavioral Realism

Age: 30 – 95 yrs (peak ≈ 63 yrs), matching chronic-disease demographics.
Gender: Balanced male/female ratio.
Socioeconomics: Tri-modal poverty distribution mirroring urban–suburban–rural mix.
Language / Race: Predominantly English and Spanish speakers with diverse representation.
Smoking: Higher prevalence in lower-income groups – reflecting known public-health gradients.

These parameters yield statistically credible yet privacy-safe population heterogeneity.

Comorbidity Architecture and Severity Representation

Fifteen chronic conditions form correlated clusters, producing realistic co-occurrence matrices:

Cluster	Typical Relationships	Observable Effects
Metabolic	Diabetes ↔ CKD ↔ Hyperlipidemia ↔ IHD	Elevated Glucose and BUN/Cr pairs
Cardiac	CHF ↔ Atrial Fibrillation ↔ IHD	More admissions + lower Albumin
Pulmonary	Asthma ↔ COPD (overlap ≈ 12 %)	Mild neutrophilia and increased WBC
Neuro-Aging	Alzheimer’s Dementia ↔ Age > 75	Higher severity and longer LOS
Psychosomatic	Depression ↔ Arthritis	Slightly elevated health-care use

The Severity Index (number of chronic conditions per patient) provides a simple, interpretable illness-burden metric.

Admissions rise monotonically with severity (r ≈ 0.9).
Lab abnormalities intensify with severity – ↑ Cr/BUN, ↓ Albumin and Hemoglobin – reflecting progressive multisystem stress.

Physiologic Coherence in Laboratories

Laboratory values are not independent; they respect medical equations and covariances:

Hematology

Hct ≈ 3× Hgb (r ≈ 0.95)
Derived indices (MCV, MCH, MCHC, RDW) remain balanced.
CKD and Cancer patients show mild anemia and higher RDW.

Electrolytes & Acid–Base

AG ≈ Na – (Cl + CO₂) ± 0.6 mmol/L.
Potassium independent within 3 – 6 mmol/L range.

Renal Panel

BUN ↔ Creatinine (r ≈ 0.8).
CKD cases show BUN ≥ 20 mg/dL and Cr ≥ 1.1 mg/dL.

Proteins & Liver Function

Total Protein = Albumin + Globulin (identity holds).
Albumin declines with cardiac disease severity.
Calcium tracks Albumin (Δ ≈ 0.1 mg/dL per 1 g/dL Alb change).

Inflammatory Markers

WBC and Neutrophils rise in COPD / Asthma.
Absolute counts sum to total WBC exactly.

Urinalysis

SG 1.014 – 1.028; pH 4.5 – 7.5.
CKD patients slightly higher Urine WBC.

Across tens of thousands of records, < 0.5 % fall outside plausible ranges.

Statistical Integrity Across Tables

1 : 1 linkage between PatientID and AdmissionID.
Temporal logic: no overlapping admissions per patient.
Lab frequency: 1 – 2 measurements per hospital day.
Scalability: distributions and correlations hold from 100 → 10 000 patients.

Empirical Validation

Visual and statistical checks confirm realism:

Age and severity histograms match population expectations.
Admissions vs Severity scatter shows monotonic trend.
Condition prevalence ≈ Hypertension > Hyperlipidemia > Diabetes > Arthritis > Depression.
Co-occurrence heatmaps show logical metabolic and cardiac clusters.

Dataset Scales

Population	Patients	Admissions (≈)	Diagnoses (≈)	Labs (≈)	Avg # Conditions / Patient	Avg Admissions / Patient
Small	100	≈ 400	≈ 800	≈ 60 000	3.1	4.0
Medium	1 000	≈ 4 000	≈ 8 000	≈ 600 000	3.2	4.0
Large	10 000	≈ 40 000	≈ 80 000	≈ 6 000 000	3.2	4.0

Analytics Gallery

Example visuals in analytics/ folder:

age_hist.png – Age distribution
severity_hist.png – Chronic condition counts
condition_prevalence_bar.png – Top 15 conditions
condition_cooccurrence_heatmap.png – Comorbidity matrix

Applications

ML training and feature testing on synthetic EHR data
Educational modules for data science and epidemiology
Pipeline and dashboard validation for multi-table databases
Research on correlation preservation and synthetic data utility

Disclaimer: All records are artificial. No real patient data were used, referenced, or replicated in any form. The datasets are generated for public research and educational purposes only.

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