A Hybrid AI and Rule-Based Decision Support System for Disease Diagnosis and Management Using Labs

2026-03-16

AI Summary

This work proposes an interpretable clinical decision support system that integrates a clinically validated rule base with a multi-class AI model to address the challenges of diagnostic errors arising from complex clinical information and the limited ability of existing tools to effectively combine laboratory data with medical knowledge. Leveraging routine laboratory results from over 590,000 patients, the system covers 59 diseases mapped to 37 ICD-10 codes. By synergistically combining a rule engine with a medical knowledge graph for joint reasoning, it automatically suggests plausible diagnoses and corresponding confirmatory tests. This approach significantly enhances diagnostic consistency and transparency while effectively narrowing the differential diagnosis space, thereby reducing the risk of misdiagnosis.

Technology Category

• Knowledge Representation and Reasoning: Diagnosis and Abductive Reasoning
• Humans and AI: Planning and Decision Support for Human-Machine Teams
• Reasoning under Uncertainty: Other Foundations of Reasoning under Uncertainty

Application Category

• Semantics and Knowledge: Methods, algorithms and applications for the development of semantic models, knowledge graphs and other forms of structured data models with machine-interpretable semantics
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Economics, Online Markets and Human Computation: Data quality aspects of human-annotated datasets

Abstract

This research paper outlines the development and implementation of a novel Clinical Decision Support System (CDSS) that integrates AI predictive modeling with medical knowledge bases. It utilizes the quantifiable information elements in lab results for inferring likely diagnoses a patient might have. Subsequently, suggesting investigations to confirm the likely diagnoses -- an assistive tool for physicians. The system fuses knowledge contained in a rule-base expert system with inferences of data driven predictors based on the features in labs. The data for 593,055 patients was collected from 547 primary care centers across the US to model our decision support system and derive Real-Word Evidence (RWE) to make it relevant for a large demographic of patients. Our Rule-Base comprises clinically validated rules, modeling 59 health conditions that can directly confirm one or more of diseases and assign ICD-10 codes to them. The Likely Diagnosis system uses multi-class classification, covering 37 ICD-10 codes, which are grouped together into 11 categories based on the labs that physicians prescribe to confirm the diagnosis. This research offers a novel system that assists a physician by utilizing medical profile of a patient and routine lab investigations to predict a group of likely diseases and then confirm them, coupled with providing explanations for inferences, thereby assisting physicians to reduce misdiagnosis of patients in clinical decision-making.

Problem

• Clinical Decision Support System
• Disease Diagnosis
• Laboratory Data
• Misdiagnosis Reduction
• Real-World Evidence

Innovation

• Hybrid AI-Rule System
• Clinical Decision Support System
• Real-World Evidence
• Explainable Diagnosis
• Lab-Based Disease Prediction