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.
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。
