Exploratory application of machine learning methods on patient reported data in the development of supervised models for predicting outcomes

BMC Medical Informatics and Decision Making

Research

Deepika Verma¹  Kerstin Bach¹  Paul Jarle Mork²  Duncan Jansen³  Mannes Poel³  Wendy Oude Nijeweme d’Hollosy⁴ 

[1] Department of Computer Science, Norwegian University of Science and Technology, Trondheim, Norway;Department of Public Health and Nursing, Norwegian University of Science and Technology, Trondheim, Norway;Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Twente, The Netherlands;Faculty of Electrical Engineering, Mathematics and Computer Science, University of Twente, Twente, The Netherlands;eHealth Cluster, Roessingh Research and Development, Enschede, The Netherlands;

关键词: Machine learning;    Low-back pain;    Neck pain;    Patient-reported outcomes;    Self-reported measures;    Outcome Prediction;   

DOI  :  10.1186/s12911-022-01973-9

 received in 2021-05-18, accepted in 2022-08-22,  发布年份 2022

来源: Springer

【 摘 要 】

BackgroundPatient-reported outcome measurements (PROMs) are commonly used in clinical practice to support clinical decision making. However, few studies have investigated machine learning methods for predicting PROMs outcomes and thereby support clinical decision making.ObjectiveThis study investigates to what extent different machine learning methods, applied to two different PROMs datasets, can predict outcomes among patients with non-specific neck and/or low back pain.MethodsUsing two datasets consisting of PROMs from (1) care-seeking low back pain patients in primary care who participated in a randomized controlled trial, and (2) patients with neck and/or low back pain referred to multidisciplinary biopsychosocial rehabilitation, we present data science methods for data prepossessing and evaluate selected regression and classification methods for predicting patient outcomes.ResultsThe results show that there is a potential for machine learning to predict and classify PROMs. The prediction models based on baseline measurements perform well, and the number of predictors can be reduced, which is an advantage for implementation in decision support scenarios. The classification task shows that the dataset does not contain all necessary predictors for the care type classification. Overall, the work presents generalizable machine learning pipelines that can be adapted to other PROMs datasets.ConclusionThis study demonstrates the potential of PROMs in predicting short-term patient outcomes. Our results indicate that machine learning methods can be used to exploit the predictive value of PROMs and thereby support clinical decision making, given that the PROMs hold enough predictive power

【 授权许可】

CC BY   
© The Author(s) 2022. corrected publication 2023

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