Clinical information search solutions provider Apixio, Inc., US, has announced that it has provided funds for research to the Center for Biomedical Informatics Research (BMIR) at Stanford University for developing data driven methods to improve healthcare outcomes. The funds will support Dr. Nigam Shah, Assistant Professor of Medicine (Biomedical Informatics Research), in his research to investigate and understand the impact of utilising large scale clinical data in enhancing quality and improving healthcare enterprise.
The growing volume of medical data - both in structured and unstructured formats - is seen to present new opportunities to understand the effects and interactions between different clinical entities like drugs, adverse events and co-morbidities. However, this also presents significant challenges in terms of handling and managing the large volume of data, as well as reconciling inconsistent data arriving from disparate sources. Apixio's core technology, Medical Information Navigation Engine (MINE), seeks to provide intelligent medical search and reconciliation, enabling researchers to focus on discovering hidden associations in both structured and unstructured clinical data.
Dr. Shah's research group studies ontology-based approaches to annotate and analyse diverse unstructured information available in bio-medicine for the purpose of extracting clinically relevant information. A major focus of the research is to combine machine learning and natural language processing (NLP) approaches to unstructured data with the semantics encoded in medical ontologies to discover valuable knowledge from the unstructured portion of a medical record. Clinical knowledge extracted by such means can be used to identify the interactions between different clinical entities such as drugs and adverse events, or risk-factors and co-morbidities for patients with specific outcomes. Hidden associations can be learned from the data to provide candidate hypotheses about possible causes and to understand the predictors of health outcomes.
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