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doi: 10.53388/ghr2022-09-056
Published online: September 25, 2022
Citation: Li J, Dong X, Zhou YN, Wu HK, Li XD, Zhou XZ. Identification of Cirrhosis Subtypes Through Heterogeneous Medical Information Network. Gastroenterol & Hepatol Res. 2022;4(3):14. doi: 10.53388/ghr2022-09-056.
Liver cirrhosis is a complex and heterogeneous disease, with a mortality rate of up to 57%, resulting in 1.03 million deaths per year. The prevalence of liver cirrhosis is on the rise. Patients with liver cirrhosis have a variety of clinical phenotypes and are prone to various complications related to liver cirrhosis. Therefore, there is an urgent need to improve the early prevention and clinical management of cirrhosis and its complications.
We use a precise medical approach to analyze and characterize the complex manifestations of cirrhotic patient populations, and we propose a Heterogeneous Medical Record Network (HEMnet) that includes electronic medical records, molecular interaction networks, and domain knowledge. We train the network embedding vector on HEMnet to obtain the low-dimensional vector representation of each node. With these vectors, we enriched the original medical record and identified six subtypes of cirrhosis.
Subtype 1 is characterized by heart disease, and subtype 2 has the strongest association with metabolic-related diseases. Subtype 3 was characterized by Chronic gastritis diseases. Subtype 4 was characterized by Liver cirrhosis-related complications-serous effusion. Subtype 5 had the strongest association with hepatitis-cirrhosis-related complications diseases and gallbladder disease. Subtype 6 was most strongly associated with Liver cirrhosis-related complications and hepatic carcinoma. By assessing the human disease-gene association of each subtype, the rich phenotype and biological functions of each subtype at the gene level were matched to the disease comorbidities and clinical differences we identified through EHR.
Our approach demonstrates the utility of applying a precision medicine paradigm to cirrhosis and the prospect of extending this approach to other complexes, multifactorial diseases.
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