ACEapi

ACEapi enables advanced time-aware querying and analysis of longitudinal patient records to support electronic phenotyping, cohort building, and exploratory analyses.

Key Features:

• Temporal Query Language: Employs a specialized temporal query language that enables complex time-aware searches across longitudinal clinical datasets.
• In-Memory Patient Object Datastore: Uses an in-memory, patient object–centric datastore that stores patient objects and enables subsecond response times for datasets comprising millions of patients.
• Scalability and Performance: Configurable for scalability to handle large data volumes while allowing trade-offs between performance and computational cost.
• Clinical Knowledge Graph Integration: Supports automatic query expansion using clinical knowledge graphs to enhance search expressiveness and accuracy.
• API Language Support: Provides programmatic access via R, Python, Java, and HTTP requests.
• Data Model Support: Accepts data formatted in the Observational Medicine Outcomes Partnership Common Data Model.

Scientific Applications:

• Electronic Phenotyping: Enables definition and identification of patient cohorts based on phenotypic characteristics extracted from EHRs.
• Cohort-Building: Facilitates extraction of temporally defined patient cohorts for epidemiological studies, clinical trials, and observational research.
• Exploratory Data Analysis: Supports rapid exploratory analyses and hypothesis generation on longitudinal clinical data.

Methodology:

Leverages a patient object–centric in-memory datastore, a specialized temporal query language, and automatic query expansion via clinical knowledge graphs, and accepts OMOP-formatted data to address limitations of relational algebra–based querying and support high-performance, time-aware searches.