Predictive informatics enables researchers, analysts, physicians and decision-makers to aggregate and analyze disparate types of data, recognize patterns and trends within that data, and make more informed decisions in an effort to preemptively alter future outcomes.
Current uses of PI
Over the past decade the increased usage of electronic health records has produced vast amounts of clinical data that is now computable. Predictive informatics integrates this data with other datasets (e.g., genotypic, phenotypic) in centralized and standardized data repositories upon which predictive analytics may be conducted.
The biopharmaceutical industry uses predictive informatics (a superset of chemoinformatics) to integrate information resources to transform data into knowledge in order to make better decisions faster in the area of drug lead identification and optimization.
Scientists involved in systems biology employ predictive informatics to integrate complex data about the interactions in biological systems from diverse experimental sources.
Predictive informatics and analytics are also used in financial services, insurance, telecommunications, retail, and travel industries.
- Predictive analytics
- Informatics (academic field)
- Predictive modeling
- Biomedical informatics
This article includes a list of references, related reading or external links, but its sources remain unclear because it lacks inline citations. (November 2010) (Learn how and when to remove this template message)
- Christophe Giraud-Carrier, Burdette Pixton, and Roberto A. Rocha. (2009) "Bariatric surgery performance: A predictive informatics case study". Intell. Data Anal., 13 (5), 741–754.
- Krohn R. (2008) "Predictive informatics. Why PI is the next great opportunity in healthcare", J Healthc Inf Manag, 22(1):8–9.
- Predictive Informatics: What Is Its Place in Healthcare? Christophe G Giraud-Carrier (2009), Brigham Young University
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