Interpretable Disease Prediction based on Reinforcement Path Reasoning over Knowledge Graphs

10/16/2020
by   Zhoujian Sun, et al.
0

Objective: To combine medical knowledge and medical data to interpretably predict the risk of disease. Methods: We formulated the disease prediction task as a random walk along a knowledge graph (KG). Specifically, we build a KG to record relationships between diseases and risk factors according to validated medical knowledge. Then, a mathematical object walks along the KG. It starts walking at a patient entity, which connects the KG based on the patient current diseases or risk factors and stops at a disease entity, which represents the predicted disease. The trajectory generated by the object represents an interpretable disease progression path of the given patient. The dynamics of the object are controlled by a policy-based reinforcement learning (RL) module, which is trained by electronic health records (EHRs). Experiments: We utilized two real-world EHR datasets to evaluate the performance of our model. In the disease prediction task, our model achieves 0.743 and 0.639 in terms of macro area under the curve (AUC) in predicting 53 circulation system diseases in the two datasets, respectively. This performance is comparable to the commonly used machine learning (ML) models in medical research. In qualitative analysis, our clinical collaborator reviewed the disease progression paths generated by our model and advocated their interpretability and reliability. Conclusion: Experimental results validate the proposed model in interpretably evaluating and optimizing disease prediction. Significance: Our work contributes to leveraging the potential of medical knowledge and medical data jointly for interpretable prediction tasks.

READ FULL TEXT

page 1

page 3

page 8

research
08/18/2023

Causal Interpretable Progression Trajectory Analysis of Chronic Disease

Chronic disease is the leading cause of death, emphasizing the need for ...
research
02/03/2021

Disease Prediction with a Maximum Entropy Method

In this paper, we propose a maximum entropy method for predicting diseas...
research
07/03/2019

High-Throughput Machine Learning from Electronic Health Records

The widespread digitization of patient data via electronic health record...
research
04/11/2023

Characterizing personalized effects of family information on disease risk using graph representation learning

Family history is considered a risk factor for many diseases because it ...
research
09/20/2017

EMR-based medical knowledge representation and inference via Markov random fields and distributed representation learning

Objective: Electronic medical records (EMRs) contain an amount of medica...
research
04/29/2021

Leveraging Online Shopping Behaviors as a Proxy for Personal Lifestyle Choices: New Insights into Chronic Disease Prevention Literacy

Ubiquitous internet access is reshaping the way we live, but it is accom...
research
06/30/2021

Reinforcement Learning based Disease Progression Model for Alzheimer's Disease

We model Alzheimer's disease (AD) progression by combining differential ...

Please sign up or login with your details

Forgot password? Click here to reset