# Learning and inference in knowledge-based probabilistic model for medical diagnosis

arXiv: https://arxiv.org/abs/1703.09368 · HTML: https://mlsanity.com/paper/1703.09368/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 28 Mar 2017.

## Built on by (most cited first, 4 of 4)

- [Biomedical Knowledge Graph: A Survey of Domains, Tasks, and Real-World Applications](https://mlsanity.com/paper/2501.11632/) (2025)
- [Diagnosis of Acute Poisoning Using Explainable Artificial Intelligence](https://mlsanity.com/paper/2102.01116/) (2021)
- [MMLN: Leveraging Domain Knowledge for Multimodal Diagnosis](https://mlsanity.com/paper/2202.04266/) (2022)
- [Profile Generators: A Link between the Narrative and the Binary Matrix Representation](https://mlsanity.com/paper/2511.19506/) (2025)

## Abstract (arXiv)

Based on a weighted knowledge graph to represent first-order knowledge and combining it with a probabilistic model, we propose a methodology for the creation of a medical knowledge network (MKN) in medical diagnosis. When a set of symptoms is activated for a specific patient, we can generate a ground medical knowledge network composed of symptom nodes and potential disease nodes. By Incorporating a Boltzmann machine into the potential function of a Markov network, we investigated the joint probability distribution of the MKN. In order to deal with numerical symptoms, a multivariate inference model is presented that uses conditional probability. In addition, the weights for the knowledge graph were efficiently learned from manually annotated Chinese Electronic Medical Records (CEMRs). In our experiments, we found numerically that the optimum choice of the quality of disease node and the expression of symptom variable can improve the effectiveness of medical diagnosis. Our experimental results comparing a Markov logic network and the logistic regression algorithm on an actual CEMR database indicate that our method holds promise and that MKN can facilitate studies of intelligent diagnosis.