Having access to realistic data is crucial for any kind of modeling. Synthea creates realistic patient data, including the patients’ heath records in a variety of formats, with varying levels of complexity. 1 No.Synthea is an open-source, synthetic patient generator that models up to 10 years of the medical history of a healthcare system. International Journal of Population Data Science: Vol. Treating heterogeneity and uncertainty in data integration: study on Brazilian healthcare databases. Low-density lipoprotein cholesterol and atrial fibrillation A Mendelian randomization study using UK-Biobank data Identification and validation of myocardial infarction and stroke outcomes at scale in UK Biobank 1 (2017): Conference Proceedings for International Population Data Linkage Conference 2016 Methods for enhancing the reproducibility of clinical epidemiology research in linked electronic health records: results and lessons learned from the CALIBER platform 2 (2019): IJPDS Special Issue: Data Centre ProfilesĪ Position Statement on Population Data Science: ![]() The Center for Data and Knowledge Integration for Health (CIDACS) The tool can be used to create realistic datasets for evaluating clustering approaches. The evaluating results suggest that the difficulty of the cluster data can be user determined. Smaller percent of noise variables increase accuracy though not significantly (coefficient 0.42).ĬlustEHR offers realistic mixed data types as well as outcomes which are frequently used to evaluate clusters when subtyping diseases. We show that high cluster separation significantly increases k-means accuracy (coefficient of 0.33). The following parameters are supported: a) number of clusters, b) number of patients in each cluster, c) number and data type of features, d) separation through defining clusters as either diseases such as COPD or dementia (high separability) or inter-disease conditions such as emphysema and chronic bronchitis within COPD (low separability), and e) noise variables through identifying variables not predictive of true cluster outcomes random forest feature importance metric. We have developed a tool for generating synthetic EHR cluster data with clinically relevant outcomes based on the rate of decline of medical observations (e.g. K-means accuracy was used as a proxy to measure cluster problem difficulty. We used a linear model to assess the relationship between these parameters and cluster problem difficulty. We evaluated clustEHR by generating multiple datasets of variable cluster separation and percentage of noise variables to reflect easy and hard clustering problems. We have created a synthetic EHR cluster generator, clustEHR, based on Synthea, a synthetic EHR generator that produces datasets (with parameterized noise and cluster separation) of known clusters and with clinically relevant patient outcomes. We developed a synthetic EHR cluster generator for benchmarking clustering algorithms. These datasets lack the complexities of real electronic health record (EHR) producing a partial assessment of the algorithm. ![]() New Clustering algorithms are benchmarked on synthetic data to assess their accuracy. Clustering algorithms are commonly used to identify disease clusters.
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