Multi-modal deep learning based on multi-dimensional and multi-level temporal data can enhance the prognostic prediction for multi-drug resistant pulmonary tuberculosis patients  

作　　者：Zhen-Hui Lu Ming Yang Chen-Hui Pan Pei-Yong Zheng Shun-Xian Zhang 

机构地区：[1]Longhua Hospital,Shanghai University of Traditional Chinese Medicine,Shanghai 200032,China

出　　处：《Science in One Health》2022年第1期6-8,共3页全健康科学（英文）

基　　金：supported by the fund of Medical Innovation Research Special Project of the Shanghai 2021"Science and Technology Innovation Action Plan"(21Y11922500,21Y11922400);the promotion and application of deep learning in traditional Chinese medicine to improve the ability and level in clinical research field(SHDC2022CRS039);the talent fund of Longhua Hospital of(LH001.007);Funding sources had no role in the design and conduct of the study,collection,management,analysis;interpretation of the data;and preparation,review,or approval of the manuscript。

摘　　要：Despite the advent of new diagnostics,drugs and regimens,multi-drug resistant pulmonary tuberculosis(MDRPTB)remains a global health threat.It has a long treatment cycle,low cure rate and heavy disease burden.Factors such as demographics,disease characteristics,lung imaging,biomarkers,therapeutic schedule and adherence to medications are associated with MDR-PTB prognosis.However,thus far,the majority of existing studies have focused on predicting treatment outcomes through static single-scale or low dimensional information.Hence,multi-modal deep learning based on dynamic data for multiple dimensions can provide a deeper understanding of personalized treatment plans to aid in the clinical management of patients.

关 键 词：MDR-PTB MULTI-MODAL Deep learning PROGNOSIS 

分 类 号：R52[医药卫生—内科学]