Research in healthcare collaborations

Improving healthcare increasingly requires data and technology.  We are working with partners at hospitals, universities, companies and governments to find betters ways to assess, treat and predict health issues.

Simulations of complex diseases

Dr. Gordon Broderick (right) with team members Hooman Sedghamiz and Dr. Matt Morris at the Center for Clinical Systems Biology at Rochester General Hospital.

Besides finding information, data science can be used to predict medical outcomes. In our collaboration with the Broderick lab at the Center for Clinical Systems Biology and Rochester General Biology, we’re supporting the team as they look to model biological circuits. These models give insights that could lead to better diagnoses and guide the design of effective treatments for complex medical conditions that defy conventional approaches. In this virtual biology environment, they collaborate with researchers around the world to tackle some of the most elusive and complex illnesses that affect the function of the endocrine, immune and nervous systems. We also successfully built Chronic Obstructive Pulmonary Disease (COPD) models by integrating putative networks with human and mouse experiment data using network simulations. In 2020, the pipeline to build the in-silico disease models will be validated further with other disease networks by collaborating with Pharma companies.

H-Graph & WebProtégé

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Elsevier developed the Healthcare Knowledge Graph, which utilizes Artificial Intelligence (AI) and Natural Language Processing (NLP) to knit together the world’s foremost clinical knowledge. Stanford Center for Biomedical Informatics Research is working with our Clinical Solutions team to build a data management and editorial system to maintain and continuously update the knowledge graph. The solution will be an extension of WebProtégé, the Stanford-developed open source solution that is used by many organizations for developing & maintaining ontologies and knowledge graphs.

Data2Person

Predicting health issues is a complex challenge that can only be solved by collaboration.  Data2Person is a large university-industry-government project being carried out by Elsevier, ExpertDoc, Pharmeon, the University of Amsterdam and the Amsterdam Medical Center, as well as the Dutch Research Council (NWO) .  By leveraging the expertise and data from all the partners, the project hopes to  improve the way we predict risks of medication interaction and design decision support systems for physicians.