So far, the mapping of health trajectories has been mostly carried-out in a hypothesis-driven manner with focus on a few diseases or specific comorbidities to index diseases, constraining the description of trajectories to their closely limited complications. Recently, large volume data analyses using centralized electronic health registries have been successfully used to describe the temporal patterns of diseases in large population groups, uncovering hitherto neglected trajectories of health that link apparently benign conditions to long-term, life-threatening conditions [Jensen et al. 2014].
The mapping and understanding of these trajectories and influencal factors is limited by several challenges:
Therefore, it is essential to develop strategies for intelligent data mining of Electronic Health Records (EHR) to map individual trajectories of health in order to support the development of prediction models and intervention strategies aimed at curbing these trajectories towards the least-unfavorable outcomes.
PatientMiner addresses these issues by developing a new strategy for linking unstructured data from EHR to map health trajectories based on an already existing running system developed in the frame of Synodos, a French collaborative project, dedicated to building technologies easy to use by medical staff for performing epidemiological studies.
With the development of EHRs, the linguistic analysis of textual data in the medical sector is receiving increasing interest. However, existing approaches vary in terms of the granularity and sophistication of linguistic processing carried out.