We have performed research that improves health outcomes and strengthens the sustainability of our health care system. Our progress is founded on artificial intelligence (AI), machine learning and data mining methodologies.
Manual data collection, and prediction for both patient management and research from length patient records is a tedious job for nurses, paramedical staff and doctors. We can develop and evaluate NLP systems to summarize unstructured patient notes in a structured form to help clinical decision making and reducing time of experts in diagnosis and prognosis of the patients. We can also build an automated disease and prognosis based model. In short, a natural language processing tool can be modified to align it for use in the clinical domain and can evaluate the applicability and feasibility in chronic disease management process.
In our approach, firstly the terms that are potential clues for a risk factor are discovered, then we extract additional information about each term (like the subject of a term) and we finally apply our rule module in order to determine if a patient is diagnosed for a specific risk factor. This tool can be user specific and we can develop this for different departments and clinical domains based on user needs. We have already developed and evaluated the tool in chronic renal patients and in dialysis dataset.