ABPS

ABPS calculates the Abnormal Blood Profile Score using hematological markers and machine learning algorithms to detect potential blood doping in athletes.

Key Features:

• Hematological Marker Integration: Computes the Abnormal Blood Profile Score by combining seven hematological markers including hemoglobin level, reticulocyte percentage, and hematocrit level.
• Machine Learning–Based Scoring: Uses two machine learning algorithms to integrate hematological parameters into a single predictive score.
• OFF-Score Calculation: Provides functions to compute the OFF-score, an additional metric used for detecting blood doping.
• Reference Test Datasets: Includes test datasets for validation and benchmarking of ABPS calculations.

Scientific Applications:

• Blood Doping Detection: Identifies abnormal hematological profiles associated with blood doping in sports.
• Athlete Biological Passport Analysis: Supports analysis of longitudinal hematological data within the Athlete Biological Passport program.
• Anti-Doping Research: Enables studies estimating the prevalence and patterns of blood doping across athlete populations.

Methodology:

ABPS processes hematological measurements using two machine learning algorithms to compute the Abnormal Blood Profile Score and related metrics such as the OFF-score from integrated blood marker data.