IIS: Workshop on Population Health Data Measurement, Representation, and Predictive Modeling.

University Of Illinois At Urbana-Champaign, Urbana IL

Investigators

Abstract

The workshop brings together a group of scientists with complementary expertise in health informatics, population health, and computer science to identify the research challenges and opportunities in population health data measurement, representation, and predictive modeling. Despite great advances in measurement, computing, and communication technologies, the health data measurement relies on legacy practices (e.g., phone surveys). In contrast, billions of persons worldwide have mobile devices, such as cell phones and music players, which contain measurement sensors and are increasingly network aware. If these devices could be appropriately employed for population health data measurement, they could revolutionize the acquisition and use of population health data. However, there are many research challenges that need to be addressed in order to make widespread use of actionable population health data. The workshop is organized around a vision of actionable data for population health. It covers a broad range of questions such as: What data should be recorded to measure everyday health? How should this data be most helpfully collected? How should the sensor data be classified into actionable data? How should the diverse sources be judged for quality? How should this data be mined and correlated? How can population data be transformed into usable knowledge? How should this data be used to develop practical health systems? How can multiple knowledge sources be integrated for multiple users? How can existing data (medical records and clinical trials) be leveraged using model-based inference to support customized decision making and refine predictive models? What is the impact of this new data on health quality and cost? Workshop participants include experts in the areas of health informatics, knowledge representation and inference, machine learning and data mining. Thw workshop aims to increase the awareness of research challenges and opportunities in health informatics in general, and population data measurement, representation, and predictive modeling in particular, among researchers in data mining, knowledge representation and inference, machine learning, text analysis, human-computer interaction, social networks and social media, semantic web, decision theory. It also aims to make researchers in health informatics, public health, and related areas better aware of the state of the art informatics approaches that could be leveraged to develop the next generation health informatics infrastructure. The workshop results, including new research challenges and opportunities in discovery informatics, will be broadly disseminated through the workshop report, publications by workshop participants, and outreach efforts through follow-up activities that engage the research community.