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Bundles and CDM BUN

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A Digital Twin is a concise, current, and true representation in silicoof a functioning real-world entity. With origins in industry manufacturing and design, it can be used to assist the assembly of many complex and interacting parts prior to an analysis. In healthcare, the creation of a Digital Twin of a person consists of assembling data from many sources and calculating the assembled result to obtain an accurate representation of an individual. That representation can then be used with the assembles of other persons to run in silicostudies. The Digital Twin makes possible more accurate population studies based upon real world data (RWD). Performing an extra layer of data reconciliation in the form of producing Digital Twins allows a population study based in Digital Twins to arrive at more accurate conclusions than when using raw data.

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SQL TableToolDescription
dt_keser_concept_childrenKESERStores hierarchical relationships between concepts for Keser analysis.
dt_keser_concept_featureKESERContains features associated with concepts for Keser analysis.
dt_keser_embeddingKESERContains embedding vectors for various concepts used in Keser.
dt_keser_featureKESERContains feature information used in Keser analysis.
dt_keser_feature_cooccurKESERContains co-occurrence data of features within patient records.
dt_keser_feature_cooccur_tempKESERTemporary table for storing intermediate co-occurrence data during Keser processing.
dt_keser_feature_countKESERContains the count of patients per feature.
dt_keser_import_concept_featureKESERStores imported concept features for Keser analysis.
dt_keser_patient_partitionKESERContains partitions of patients into training and test cohorts for parallel processing.
dt_keser_patient_period_featureKESERContains features for each patient over specific time periods.
dt_keser_phenotypeKESERContains phenotype definitions identified by Keser.
dt_keser_phenotype_featureKESERLinks features to their corresponding phenotypes in Keser.
dt_komap_base_cohortKOMAPBase cohort of patients for the KOMAP program.
dt_komap_patient_featureKOMAPContains features for each patient for KOMAP analysis.
dt_komap_phenotypeKOMAPContains phenotype definitions used in KOMAP.
dt_komap_phenotype_covarKOMAPContains covariates used in phenotype analysis for KOMAP.
dt_komap_phenotype_covar_innerKOMAPContains intermediate covariate data used during KOMAP phenotype analysis.
dt_komap_phenotype_feature_coefKOMAPContains coefficients for features used in KOMAP phenotype computation.
dt_komap_phenotype_feature_dictKOMAPDictionary of features used in KOMAP phenotype analysis.
dt_komap_phenotype_gmmKOMAPContains Gaussian Mixture Model results used in KOMAP phenotype clustering.
dt_komap_phenotype_gold_standardKOMAPContains gold standard phenotypes used for validating KOMAP computational phenotype models.
dt_komap_phenotype_patientKOMAPLinks patients to their computed phenotypes in KOMAP.
dt_komap_phenotype_sampleKOMAPContains sampled patient data for KOMAP phenotype analysis.
dt_komap_phenotype_sample_featureKOMAPContains features for each phenotype sample in KOMAP.
dt_komap_phenotype_sample_resultsKOMAPContains results of KOMAP phenotype analysis on sampled data.
DT_LOYALTY_CHARLSONLOYALTYCharlson Comorbidity Index data for loyalty cohort analysis.
DT_LOYALTY_PATHSLOYALTYPathways data for determining patient care paths in the Ontology paths associated with features used in loyalty cohort analysis.
DT_LOYALTY_PSCOEFFLOYALTYCoefficients for propensity loyalty score calculation in the loyalty cohort analysis.
DT_LOYALTY_RESULTLOYALTYResults of loyalty cohort analysis.
DT_LOYALTY_RESULT_CHARLSONLOYALTYResults of Charlson Comorbidity Index analysis for the loyalty cohort.
DT_LOYALTY_RESULT_SUMMARYLOYALTYSummary results of the loyalty cohort analysis.

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Bundles and CDM BUN