ICML 2012 Workshop on Machine Learning for Clinical Data Analysis

University Of Pittsburgh, Pittsburgh PA

Investigators

Abstract

The wealth and variety of data generated in modern medical and health-care settings present tremendous research challenges as well as opportunities in artificial intelligence and machine learning. Extensive electronic medical records - with thousands of fields recording patient conditions, diagnostic tests, treatments, outcomes, as well as narrative about the patients and care delivery - provide an unprecedented source of information. Tapping into this data source can bring clues leading to improvements in a wide range of health-care applications, such as disease modeling and early detection, chronic disease management, and efficient design of clinical trials. Intellectual Merit: The Workshop on Machine Learning for Clinical Data Analysis (http://sites.google.com/site/mlclinicaldata) will be held during the International Conference on Machine Learning (ICML), 2012, Edinburgh, Scotland on June 30-July 1, 2012. The workshop aims to bring together machine learning and informatics researchers interested in problems and applications in the clinical domain, with the goal of bridging the gap between the theory of machine learning and the needs of the health informatics applications. The award provides funds to cover the travel costs of invited speakers and graduate students. The Ph.D. student participants will be able to present their work, interact with their peers from other universities as well as hundreds of leading researchers in machine learning from around the world. In addition to attending the workshop, they will attend the technical sessions, plenary talks, and tutorials of their choice at the conference. The invited speakers will present talks covering state-of-the-art research as well as open machine learning research challenges in building predictive models from clinical data. The workshop aims to educate the machine learning research community regarding machine learning research opportunities and challenges in health care applications, especially in connection with recent electronic health record initiatives; identify new machine learning problems not previously addressed by the community; and help build a community of researchers who can advance machine learning informed by the challenges and opportunities presented by clinical data analytics. Broader Impacts. Machine learning is playing an increasingly important role in many emerging data-rich sciences and application domains, such as bioinformatics, computational biology, health informatics, and security informatics. Participation in the workskshop and the ICML and COLT conferences will enrich the education and training of student researchers at early stages in their careers. The travel awards will help broaden the participation of women and members of underrepresented minority groups within the Machine Learning and Health Informatics research communities.