![]() ![]() As the development of ML-based models continues to surge, greater attention is being given to a model’s reproducibility. ![]() The advantages of applying machine learning (ML) methods within healthcare have become increasingly evident, particularly for the purposes of predictive modeling. Our models demonstrate that site-specific customization improves predictive performance when compared to other ready-made approaches. Using a case study of COVID-19 diagnosis across four NHS Hospital Trusts, we show that all methods achieve clinically-effective performances (NPV > 0.959), with transfer learning achieving the best results (mean AUROCs between 0.870 and 0.925). We introduce three methods to do this-(1) applying a ready-made model “as-is” (2) readjusting the decision threshold on the model’s output using site-specific data and (3) finetuning the model using site-specific data via transfer learning. Different approaches have been introduced for developing models across multiple clinical sites, however less attention has been given to adopting ready-made models in new settings. As patient health information is highly regulated due to privacy concerns, most machine learning (ML)-based healthcare studies are unable to test on external patient cohorts, resulting in a gap between locally reported model performance and cross-site generalizability. ![]()
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