Establishment of a Knowledge‐and‐Data‐Driven Artificial Intelligence System with Robustness and Interpretability in Laboratory Medicine

Abstract

Laboratory medicine plays an important role in clinical diagnosis. However, no laboratory-based artificial intelligence (AI) diagnostic system has been applied in current clinical practice due to the lack of robustness and interpretability. Although many attempts have been made, it is still difficult for doctors to adopt the existing machine learning (ML) patterns in interpreting laboratory (lab) big data. Here, a knowledge-and-data-driven laboratory diagnostic system is developed, termed AI-based Lab tEst tO diagNosis (AI LEON), by integrating an innovative knowledge graph analysis framework and "mixed XGboost and Genetic Algorithm (MiXG)" technique to simulate the doctor's laboratory-based diagnosis. To establish AI LEON, we included 89 116 949 laboratory data and 10 423 581 diagnosis data points from 730 113 participants. Among them, 686 626 participants were recruited for training and validating purposes with the remaining for testing purposes. AI LEON automatically identified and analyzed 2071 lab indexes, resulting in multiple disease recommendations that involved 441 common diseases in ten organ systems. AI LEON exhibited outstanding transparency and interpretability in three universal clinical application scenarios and outperformed human physicians in interpreting lab reports. AI LEON is an advanced intelligent system that enables a comprehensive interpretation of lab big data, which substantially improves the clinical diagnosis.