Recently, a lot of data with variety factors and indicators of cognitive diseases is available for clinical research. Although the transformation of information to particular data model is straight-forward, a lot of challenges arise if data from different repositories is integrated. Since each data source keeps entities with different names and relationships at different levels of granularity, the information can be partially lost or not properly presented. It is therefore important to have a common data model that provides a unified description of different factors and indicators related to cognitive diseases. This paper proposes a hierarchical data model of patients with cognitive disorders, which keeps the semantics of the data in a human-readable format and accelerates the 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, including personal profile, anamnestic profile, related to social status, everyday habits, and head trauma history, clinical profile, describing medical investigations and assessments, comorbidities and the most likely diagnose, and 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. It is intended to be used by researchers in the field of Biostatistics, Bioinformatics, Neuroscience, etc. supporting them in content mapping and data integration from different datasets.