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

Abstract

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 functio n 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 efficie ntly 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.

Machine Learning Algorithms Implementation in the Healthcare System as a Prospective Area for Science, Healthcare, and Business

“…A very prospective field is using external knowledge graphs for improving medical diagnosis results [48]. Knowledge graph g = {N, R} is a set of medical entities as nodes (N) and relations between these entities {R}.…”

Integration of Healthcare domain Ontologies using Bayesian Networks

“…But independence amongst the symptoms is not always favorable for efficient diagnosis conclusion as there will be strong co-relation between the symptoms and related diseases [12] most of the time. In-order to ensure the completion of information in the available ontologies, either they should be enriched with additional knowledge through automated techniques [13] or to be reconstructed with probability details [14] along with a check of special constraints [15]. The data conversion from MySQL format to onto-graph can be done through the ready tool -Owlready [16] easily.…”

Bayesian Network‐Based Knowledge Graph Inference for Highway Transportation Safety Risks

“…All of the above-completion, fast retrieval, and answering problems related to knowledge graphs-can be understood as the scenario applications of knowledge inference. (4) Hybrid models: Jiang et al [14] developed a method for representing knowledge based on weighted knowledge graphs that was then combined with the probabilistic graphical model to establish a medical diagnosis knowledge network. A path sorting algorithm-based random walk model was proposed by Liu et al [15] that performed knowledge inference on the semantics of sentences regarding the inverse relationship from object to subject.…”