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

arXiv cs.AI / 3/17/2026

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Key Points

The paper introduces a hybrid Clinical Decision Support System that fuses a rule-based expert system with data-driven AI predictors to infer likely diagnoses from routine lab results and propose confirmatory investigations.
It uses real-world evidence from 593,055 patients across 547 primary care centers in the US to calibrate the model and ensure broad demographic applicability.
The rule base covers 59 clinically validated conditions with ICD-10 mappings, while the AI classifier handles 37 ICD-10 codes grouped into 11 lab-based categories and provides explanations for its inferences.
The system is designed to assist physicians in decision-making and reduce misdiagnosis by suggesting likely diseases and recommended investigations, with explanations to support trust and adoption.

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.