ReviewScience in One Health2022
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.
Review in Science in One Health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Article
- Artificial intelligence approaches to predicting treatment non-adherence in chronic diseases: a narrative review.Frontiers in digital health · 2026Review
- Schistosomiasis transmission in Zimbabwe: Modelling based on machine learning.Infectious Disease Modelling · 2024Article
- Global, regional, and national burden of HIV-negative tuberculosis, 1990-2021: findings from the Global Burden of Disease Study 2021.Infectious diseases of poverty · 2024Article
- Epidemiological features and temporal trends of the co-infection between HIV and tuberculosis, 1990-2021: findings from the Global Burden of Disease Study 2021.Infectious diseases of poverty · 2024Article
- Efficacy of the 'Five-Needle' method for pancreatojejunostomy in laparoscopic pancreaticoduodenectomy: an observational study.Frontiers in oncology · 2024Article
- Power of One Health: the first year ofScience in One Health · 2023Article
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Authors and funding
5 authors.
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Abstract
Despite the advent of new diagnostics, drugs and regimens, multi-drug resistant pulmonary tuberculosis (MDR-PTB) 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.
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