Patient Trajectory Modelling in Longitudinal Data: A Review on Existing Solutions

Physicians use electronic health records to monitor patient health and make more accurate prognoses, diagnoses and clinical decisions. However, with the ever increasing amounts of information stored for each patient, manually processing and digesting all this information becomes increasingly challenging, which opens an opportunity for developing clinical decision support systems such as patient trajectory modelling solutions. Patient trajectory modelling is a topic of growing research interest due to its potential to help improving health care quality by fostering preventive medicine practices, since an earlier disease diagnosis can enable better disease management and earlier intervention, along with an improved resource allocation. In this paper, we review recent approaches for patient trajectory prediction, performing a comparison based on three key aspects: 1) what is the core of the developed approach, 2) what type of data is used in the work, and 3) how is temporal information handled in the proposed solution. The resulting selection of works herein presented illustrates the current paradigm in patient trajectory modelling, and provides an overview on some of the existing challenges in this field.