Sensors, Vol. 22, Pages 131: Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network

Sensors, Vol. 22, Pages 131: Measuring Patient Similarity on Multiple Diseases by Joint Learning via a Convolutional Neural Network Sensors doi: 10.3390/s22010131 Authors: Sang Ho Oh Seunghwa Back Jongyoul Park Patient similarity research is one of the most fundamental tasks in healthcare, helping to make decisions without incurring additional time and costs in clinical practices. Patient similarity can also apply to various medical fields, such as cohort analysis and personalized treatment recommendations. Because of this importance, patient similarity measurement studies are actively being conducted. However, medical data have complex, irregular, and sequential characteristics, making it challenging to measure similarity. Therefore, measuring accurate similarity is a significant problem. Existing similarity measurement studies use supervised learning to calculate the similarity between patients, with similarity measurement studies conducted only on one specific disease. However, it is not realistic to consider only one kind of disease, because other conditions usually accompany it; a study to measure similarity with multiple diseases is needed. This research proposes a convolution neural network-based model that jointly combines feature learning and similarity learning to define similarity in patients with multiple diseases. We used the cohort data from the National Health Insurance Sharing Service of Korea for the experiment. Experimental results verify that th...
Source: Sensors - Category: Biotechnology Authors: Tags: Article Source Type: research