A Similarity-Guided Framework for Error-Driven Discovery of Patient Neighbourhoods in EMA Data

Recent advances in technology and societal changes have increased the amount of patient data that is being collected remotely, outside of hospitals. As technology enables the ability to collect Ecological Momentary Assessments (EMAs) of patient symptoms remotely, personalised predictors have become especially relevant in the field of medicine. However, focusing a predictive model on a single patient’s data comes with sometimes extreme trade-offs on the amount of data available for training. While it is possible to mitigate this loss of data by including data from similar patients, the concept of similarity itself may be poorly defined in cases where patient data are available in two modalities - one that is fixed and relatively static (for e.g.: age, gender, etc.), and those that are more dynamic (instantaneous symptom severity). Including data from users with similar EMA data and disease characteristics has been explored with respect to building personalised predictors of the near future of a patient. We propose a method to build personalised predictors by discovering a neighbourhood for each user that decreases the prediction error of a model over that user’s data. This method is useful not just for building better personalised predictors, but may also serve as a starting point for future investigations into what properties are shared by patients whose EMA data predict each other. We test our method on two EMA datasets, and show that our proposed method achieves significantly better RMSE than a single non-personalised global model, and that our framework provides better predictions for 82%–89% of the users compared to the global model for two datasets.