Deep Patient Trajectory Analysis: Learning from electronic health records

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Electronic health records (EHR) have recently matured into an enormous source of routinely collected information. Today, a single EHR can contain comprehensive medical histories for millions of patients, logging and collating data collected from all levels of healthcare over the course of decades from multiple healthcare institutions. Such a dataset can contain terabytes of information, with billions of entries recorded in a complex underlying data structure. In the past, classical survival analysis techniques have been used extensively to make predictions about the future of patients based on relatively small quantities of information. In my work, I am investigating how we can leverage much more of the information available in electronic health records, by adapting survival analysis techniques to a deep learning framework, to give healthcare providers more accurate survival distribution predictions and therefore improve their ability to make effective healthcare decisions. The primary aim of this research is to produce deep learning models that are capable of processing a patient's entire medical history and returning a precise distribution for the time to an event of interest. This will require several independent components. Firstly, we will develop a robust method of pre-processing multi-modal, multi-source longitudinal EHR data. Secondly, we will need a suitable deep learning architecture that can identify the potentially complicated patterns in this data to create an accurate model of a patient and their susceptibility to the event of interest. Finally, we want our models to output a suitable survival distribution for the time to the event of interest. While there has already been some research into applying deep learning to EHR data, this methodology is novel in that it goes further than previous work, which has only looked into the probability of events occurring between discrete timepoints. I also plan to extend the work further by adapting more advanced survival analysis techniques to a deep learning framework, by exploring competing risks and multi-state models as well as scoring for distribution family selection. This research is being done in collaboration with the NIHR ARC Northwest London research group and methods developed will be applied to data from the Northwest London Whole System Integrated Care (WSIC) database, with the aim of providing local healthcare providers with improved tools to understand and improve their care. The first case studies we plan to test our methods on include investigating time to events and multi-state modelling for patients suffering from Diabetes, heart-conditions as well as those with multi-morbidities. This project falls within the EPSRC healthcare technologies research area.