External validation of prediction models for patient-reported outcome measurements collected using the selfBACK mobile app. (February 2023) - Verma, Deepika; Bach, Kerstin; Mork, Paul Jarle
Abstract: Background : External validation is essential in examining the disparities in the training and validation cohorts during the development of prediction models, especially when the application domain is healthcare-oriented. Currently, the use of prediction models in healthcare research aimed at utilising the under-explored potential of patient-reported outcome measurements (PROMs) is limited, and few are validated using external datasets. Objective : To validate the machine learning prediction models developed in our previous work [29] for predicting four pain-related patient-reported outcomes from the selfBACK clinical trial datasets. Methods : We evaluate the validity of three pre-trained prediction models based on three methods— Case-Based Reasoning, Support Vector Regression, and XGBoost Regression—using an external dataset that contains PROMs collected from patients with non-specific neck and or low back pain using the selfBACK mobile application. Results : Overall, the predictive power was low, except for prediction of one of the outcomes. The results indicate that while the predictions are far from immaculate in either case, the models show ability to generalise and predict outcomes for a new dataset. Conclusion : External validation of the prediction models presents modest results and highlights the individual differences and need for external validation of prediction models in clinical settings. There is need for further development in this area of machine learning application and patient-centred care.