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

TL;DR

This paper presents a hybrid AI and rule-based clinical decision support system that leverages lab results and validated medical rules to predict and confirm diagnoses, aiming to reduce misdiagnosis in healthcare.

Contribution

It introduces a novel integrated system combining AI predictive models with expert rules for disease diagnosis using lab data and real-world evidence.

Findings

Model covers 37 ICD-10 codes across 11 categories.

Utilizes data from over 593,000 patients.

Provides explainable inferences to assist physicians.

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.