Explainable Diagnosis Prediction through Neuro-Symbolic Integration

Authors

Qiuhao Lu
Rui Li
Elham Sagheb
Andrew Wen
Jinlian Wang
Liwei Wang
Jungwei W Fan
Hongfang Liu

Language

English

Publication Date

1-1-2025

Journal

AMIA Summits on Translational Science Proceedings

PMID

40502272

PMCID

PMC12150699

PubMedCentral® Posted Date

6-10-2025

PubMedCentral® Full Text Version

Post-print

Abstract

Diagnosis prediction is a critical task in healthcare, where timely and accurate identification of medical conditions can significantly impact patient outcomes. Traditional machine learning and deep learning models have achieved notable success in this domain but often lack interpretability which is a crucial requirement in clinical settings. In this study, we explore the use of neuro-symbolic methods, specifically Logical Neural Networks (LNNs), to develop explainable models for diagnosis prediction. Essentially, we design and implement LNN-based models that integrate domain-specific knowledge through logical rules with learnable weights and thresholds. Our models, particularly Mmulti-pathway and Mcomprehensive, demonstrate superior performance over traditional models such as Logistic Regression, SVM, and Random Forest, achieving higher accuracy (up to 80.52%) and AUROC scores (up to 0.8457) in the case study of diabetes prediction. The learned weights and thresholds within the LNN models provide direct insights into feature contributions, enhancing interpretability without compromising predictive power. These findings highlight the potential of neuro-symbolic approaches in bridging the gap between accuracy and explainability in healthcare AI applications. By offering transparent and adaptable diagnostic models, our work contributes to the advancement ofprecision medicine and supports the development of equitable healthcare solutions. Future research will focus on extending these methods to larger and more diverse datasets to further validate their applicability across different medical conditions and populations.

Published Open-Access

yes

Recommended Citation

Lu, Qiuhao; Li, Rui; Sagheb, Elham; et al., "Explainable Diagnosis Prediction through Neuro-Symbolic Integration" (2025). Faculty, Staff and Student Publications. 779.
https://digitalcommons.library.tmc.edu/uthshis_docs/779