Predicting nutrition and environmental factors associated with female reproductive disorders using a knowledge graph and random forests.

ABSTRACT:

Objective

Female reproductive disorders (FRDs) are common health conditions that may present with significant symptoms. Diet and environment are potential areas for FRD interventions. We utilized a knowledge graph (KG) method to predict factors associated with common FRDs (e.g., endometriosis, ovarian cyst, and uterine fibroids).

Materials and methods

We harmonized survey data from the Personalized Environment and Genes Study on internal and external environmental exposures and health conditions with biomedical ontology content. We merged the harmonized data and ontologies with supplemental nutrient and agricultural chemical data to create a KG. We analyzed the KG by embedding edges and applying a random forest for edge prediction to identify variables potentially associated

SUBMITTER: Chan LE 

PROVIDER: S-EPMC10371183 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature