Abstract
to identify the defining characteristics of Ineffective airway clearance with better predictive power using classification trees. the predictive power of the defining characteristics of Ineffective airway clearance was evaluated based on classification trees generated from the data of 249 children with acute respiratory infection. Ineffective cough and adventitious breath sounds were identified as the main defining characteristics when screening for Ineffective airway clearance in accordance with trees based on three different computational algorithms. Ineffective coughing and adventitious breath sounds had better predictive capacity for Ineffective airway clearance in the sample.
Highlights
OBJECTIVEMistakes in the diagnostic inference can compromise care plans and lead to unsatisfactory clinical outcomes for the patient, especially in complex clinical situations with multiple signs and symptoms or similar nursing diagnoses
Classification trees (CT) are graphical tools based on computational algorithms used to calculate conditional probabilities for a specific event, according to the presence / absence of a minimum set of variables
The classification trees (CT) format presents leaf-nodes labeled with a value that correspond to the attribute of a class, and internal nodes represent descriptive attributes
Summary
OBJECTIVEMistakes in the diagnostic inference can compromise care plans and lead to unsatisfactory clinical outcomes for the patient, especially in complex clinical situations with multiple signs and symptoms or similar nursing diagnoses. Strategies to identify a minimum set of clinical indicators associated with the specific clinical condition can improve the decision-making process. In this context, classification trees (CT) are graphical tools based on computational algorithms used to calculate conditional probabilities for a specific event (nursing diagnosis), according to the presence / absence of a minimum set of variables (defining characteristics). The CT format presents leaf-nodes labeled with a value that correspond to the attribute of a class (presence / absence of a nursing diagnosis), and internal nodes represent descriptive attributes (presence / absence of defining characteristics). The branches of the base are considered terminal nodes, and they indicate that another division of the tree is not possible or reliable[2]
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