Cognitive Impairment and Dementia Data Modelling

Abstract

Recently, a huge amount of data is available for clinical research on cognitive diseases. A lot of challenges arise when data from different repositories should be integrated. Since data entities are stored with different names at different levels of granularity, a common data model is needed, providing a unified description of different factors and indicators of cognitive diseases. This paper proposes a common hierarchical data model of patients with cognitive disorders, which keeps the semantics of the data in a human-readable format and accelerates interoperability of clinical datasets. It defines data entities, their attributes and relationships related to diagnosis and treatment. The data model covers four main aspects of the patient's profile: (1) personal profile;(2) anamnestic profile, including social status, everyday habits, and head trauma history; (3) clinical profile, describing medical investigations and assessments, comorbidities and the most likely diagnose; and (4) treatment profile with prescribed medications. It provides a native vocabulary, improving data availability, saving efforts, accelerating clinical data interoperability and standardizing data to minimize risk of rework and misunderstandings. The data model enables the application of machine learning algorithms by helping scientists to understand the semantics of information through a holistic view of patient.