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✅   Development and validation of an interpretable machine learning model for predicting Philadelphia chromosome-positive acute lymphoblastic leukaemia using clinical and laboratory parameters: a single-centre retrospective study

Health

Objective To develop and validate a prediction model of Philadelphia chromosome-positive acute lymphoblastic leukaemia (Ph+ALL). Design A single-centre retrospective study. Participants This study analysed 471 newly diagnosed patients with ALL at the Second Affiliated Hospital of Army Medical University from January 2014 to December 2023. Methods Clinical and laboratory parameters were collected, and the important characteristic parameters were selected using BorutaShap. Multiple machine learning (ML) models were constructed and optimised by using the active learning (AL) algorithm. Performance was evaluated using the area under the curve (AUC), comprehensive indicators and decision curve analysis. The interpretability of the model was evaluated by using SHapley Additive Interpretation (SHAP), and external validation was conducted on an independent ...

BMJ     Jun 28, 2025