Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study-Reference-Cited by-同舟云学术

Machine learning model predicts airway stenosis requiring clinical intervention in patients after lung transplantation: a retrospective case-controlled study

Published:2024-08-19 Issue:1 Volume:24 Page:
ISSN:1472-6947
Container-title:BMC Medical Informatics and Decision Making
language:en
Short-container-title:BMC Med Inform Decis Mak

Author:

Tian Dong,Zuo Yu-Jie,Yan Hao-Ji,Huang Heng,Liu Ming-Zhao,Yang Hang,Zhao Jin,Shi Ling-Zhi,Chen Jing-Yu

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1186/s12911-024-02635-8.pdf

Reference36 articles.

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2. Hardy JD, Webb WR, Dalton ML Jr, Walker GR Jr. Lung homotransplantation in man. Jama. 1963;186:1065–74.

3. Date H, Trulock EP, Arcidi JM, Sundaresan S, Cooper JD, Patterson GA. Improved airway healing after lung transplantation. An analysis of 348 bronchial anastomoses. J Thorac Cardiovasc Surg. 1995;110(5):1424–32 discussion 32–3.

4. Crespo MM, McCarthy DP, Hopkins PM, Clark SC, Budev M, Bermudez CA, et al. ISHLT Consensus Statement on adult and pediatric airway complications after lung transplantation: Definitions, grading system, and therapeutics. J Heart Lung Transplant. 2018;37(5):548–63.

5. Santacruz JF, Mehta AC. Airway complications and management after lung transplantation: ischemia, dehiscence, and stenosis. Proc Am Thorac Soc. 2009;6(1):79–93.