Algorithms

Algorithmic logic - typically implemented as R code files - when used in combination with concept sets, will define a cohort of individuals with specific conditions, diseases, or clinical characteristics. These algorithmically defined variables, sometimes called computable phenotypes, were developed as independent variables, dependent variables, or covariates within research studies.

Each Variable Definition in this collection corresponds to a study variable explored by PEDSnet, ensuring consistency and reproducibility across analyses. Exploration of this collection may help potential collaborators conceptualize the breadth of PEDSnet clinical data and understand how variables have been used in prior research, and support current investigators by providing existing code reuse. This variable library is continually evolving, updated with each new publication or dissemination product. You can browse the full collection below or explore specific topics by navigating our twenty clinical subject domains.

Domains in this Collection

Now showing 1 - 10 of 20
  • Cardiovascular Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of conditions affecting the heart and blood vessels.
  • Congenital, Hereditary, and Neonatal Disease and Abnormality Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of conditions present at birth, inherited disorders, or abnormalities arising in the neonatal period.
  • Digestive System Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of disorders affecting the gastrointestinal tract and related organs.
  • Endocrine System Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of disorders involving hormone-producing glands and hormonal imbalances.
  • Eye Condition Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of conditions that affect the eyes or vision.
  • Hemic and Lymphatic Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of disorders of the blood, bone marrow, and lymphatic system.
  • Immune System Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of autoimmune, immunodeficiency, or other immune-related disorders.
  • Infection Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of diseases caused by pathogenic microorganisms such as bacteria, viruses, fungi, or parasites.
  • Musculoskeletal Algorithms  
    Variables that define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of conditions affecting muscles, bones, joints, and connective tissues.
  • Neoplasm Algorithms  
    Variables which define cohorts of patients based on the presence, type, diagnosis, treatment, and progression of benign or malignant tumors.