Stanford University and Apixio Interpret Big Data for Healthcare

Apixio funds research to discover associations and understand patterns in clinical data

SAN MATEO, Calif.--(BUSINESS WIRE)--Apixio Inc, the leading provider of search solutions for clinical information 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 health care outcomes. Funds will support Dr. Nigam Shah, Assistant Professor of Medicine (Biomedical Informatics Research), in his research to investigate and understand the impact of utilizing large scale clinical data in enhancing quality and improving health care enterprise.

“Easy access to reconciled and ontology-indexed data allows researchers to understand patterns in clinical data that are common in patients with a certain outcome and provide candidate hypotheses about the possible causes as well as predictors of that outcome”

The growing volume of medical data both in structured and unstructured formats presents exciting new opportunities to understand the effects and interactions between different clinical entities like drugs, adverse events, and comorbidities, 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), provides intelligent medical search and reconciliation, enabling researchers to focus on discovering hidden associations in both structured and unstructured clinical data.

“Easy access to reconciled and ontology-indexed data allows researchers to understand patterns in clinical data that are common in patients with a certain outcome and provide candidate hypotheses about the possible causes as well as predictors of that outcome,” says Dr. Shah.

Dr. Nigam H. Shah's research group studies ontology-based approaches to annotate and analyze 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 comorbidities 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.

About Apixio

Privately held and based in San Mateo, California, Apixio Inc. is the leading provider of search solutions for standards based clinical information. Apixio is improving healthcare by providing instant access to relevant clinical information, anywhere.