PSB is offering four workshops during the meeting (exact dates to be determined). These workshops were created to provide an opportunity for a gathering that will not be based on peer-reviewed papers included in the proceedings book. The workshops will consist of presentations by invited speakers. Abstract submissions for the workshops will be evaluated by the workshop co-chairs.
Each workshop has a chair who is responsible for organizing submissions. Please contact the specific session chair relevant to your interests for further information. Links on each of the session titles below lead to more detailed calls for participation.
With the growing importance of Precision Medicine Initiatives in biomedical informatics, it has become imperative that all segments of society become fully engaged and represented. Diverse patient cohorts, a diverse informatics workforce, and improved approaches are all an essential to ensuring equitable benefit to members of society. This challenge present wide-ranging informatics opportunities that are addressed in this workshop.
Contact: William M. Southerland
Email: DDBIatPSB18 at gmail.com
Precision Medicine focuses on collecting and using individual-level data to improve healthcare outcomes. To date, research efforts have been motivated by molecular-scale measurements, such as incorporating genomic data into clinical use. In many cases however, environmental, social, and economic factors are much more predictive of health outcomes, yet are not systematically used in clinical practice due to the difficulties in measurement and quantification. Advances in both the availability of electronic health information, environmental exposure data, and the more systematic use of geo-coding now provide ways to systematically assess community-level indicators of health, and link these factors to electronic health records for evaluating their influence on disease outcomes. In this workshop, we will introduce attendees to new electronic sources of community-level data, and provide insight into their utility and validity when compared with gold-standard data collection approaches.
Contact: Will Bush
Email: wsb36 at case.edu
The goals of this workshop are to discuss challenges in explainability of current Machine Learning and Deep Analytics (MLDA) used in biocomputing, and ways to improve it. Explainability in MLDA refers to easy-to-use information explaining why and how the MLDA approach made its decisions intended for experts and non-experts alike. Given the increased importance and use of MLDA methods in biocomputing, this is a critical issue in achieving wider MLDA adoption and improving its effective usage.
Contact: Dragutin Petkovic
Email: petkovic at sfsu.edu
The workshop goal is to exchange novel approaches for evaluating data quality (DQ) and innovative ways of reporting DQ findings in a standardized, readily accessible format across multiple data partners. Specific topics are:
Email: vojtech dot huser at nih dot gov