CogStack is an information retrieval and extraction platform developed by researchers at the NIHR Maudsley Biomedical Research Centre. It implements enterprise search, natural language processing, analytics and visualisation technologies to unlock health records and assist in clinical decision making and research.
Electronic health record systems are often closed, proprietary and contain incomplete and unstructured data. The result is that the wealth of information potentially available within health records is often inaccessible and underused.
The CogStack platform is an important tool at South London and Maudsley NHS Foundation Trust which can provide near real time access to data from patients and address pressing medical questions. The platform can quickly analyse medical notes for patients to search for queries such as ‘patients with dementia’ or even more specifically ‘female patients with dementia’, which can then help answer important questions, for example on the safety of proposed new treatments.
CogStack implements new data mining techniques within NHS Trusts – specifically, the ability to search any clinical data source (unstructured and structured), and natural language processing (NLP) applications developed to automate information extraction of medical concepts.
These tools allow clinical text to be searched for specific terms using simple or complex syntax, rapidly retrieving the data needed to answer complex queries such as “has this patient received any high-cost treatments that have not been captured in their discharge summary?” or “provide me with patients with early onset Parkinson’s disease”.
So far at King’s College Hospital over twelve million free text documents and over 250 million diagnostic results and reports have been processed within CogStack. The speed at which queries can be developed and results returned and refined is very powerful, for example allowing clinical trials to find and recruit patients who would otherwise have been difficult to locate.
Find out more about CogStack in this short film:
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