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Table 3 General characteristics of reviewed articles

From: Machine learning-based techniques to improve lung transplantation outcomes and complications: a systematic review

Main objectives

Frequency

Percentage

Predict the acute disease events after transplantation

4

25.00%

Predict survival rate

4

25.00%

Predict recipient-donor matching

2

12.50%

Predict pulmonary functions/ pulmonary symptoms after transplantation

2

12.50%

Predict primary graft dysfunction after lung transplantation

1

6.25%

Determine the role of infection in rejection

1

6.25%

Predict the risk factors for transplantation

1

6.25%

Predict quality of life

1

6.25%