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-Reference-Cited by-同舟云学术

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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

Published:2022-11 Issue: Volume:1 Page:100004
ISSN:2949-7043
Container-title:Science in One Health
language:en
Short-container-title:Science in One Health

Author:

Lu Zhen-Hui,Yang Ming,Pan Chen-Hui,Zheng Pei-Yong,Zhang Shun-Xian^ORCID

Publisher

Elsevier BV

Reference14 articles.

1. Impact of multiple policy interventions on the screening and diagnosis of drug-resistant tuberculosis patients: a cascade analysis on six prefectures in China;Ding;Infect Dis Poverty,2021

2. Efficacy of integrating short-course chemotherapy with Chinese herbs to treat multi-drug resistant pulmonary tuberculosis in China: a study protocol;Zhang;Infect Dis Poverty,2021

3. Epidemiological characteristics of rifampicin-resistant tuberculosis in students - China, 2015-2019;Su;China CDC Wkly,2021

4. One Health: new evaluation framework launched;Zhang;Nature,2022

5. Towards a global One Health index: a potential assessment tool for One Health performance;Zhang;Infect Dis Poverty,2022

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