Summary of second annual MCBK public meeting: Mobilizing Computable Biomedical Knowledge—A movement to accelerate translation of knowledge into action

The volume of biomedical knowledge is growing exponentially and much of this knowledge is represented in computer executable formats, such as models, algorithms and programmatic code. There is a growing need to apply this knowledge to improve health in Learning Health Systems, health delivery organizations, and other settings. However, most organizations do not yet have the infrastructure required to consume and apply computable knowledge, and national policies and standards adoption are not sufficient to ensure that it is discoverable and used safely and fairly, nor is there widespread experience in the process of knowledge implementation as clinical decision support. The Mobilizing Computable Biomedical Knowledge (MCBK) community formed in 2016 to address these needs. This report summarizes the main outputs of the Second Annual MCBK public meeting, which was held at the National Institutes of Health on July 18‐19, 2019 and brought together over 150 participants from various domains to frame and address important dimensions for mobilizing CBK.Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/1/lrh2-sup-0001-supinfo.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/2/lrh210222.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154970/3/lrh210222_am.pd

A study of collaboration among medical informatics research laboratories

Abstract The InterMed Collaboratory involves five medical institutions (Stanford University, Columbia University, Brigham and Women's Hospital, Massachusetts General Hospital, and McGill University) whose mandate has been to join in the development of shared infrastructural software, tools, and system components that will facilitate and support the development of diverse, institution-specific applications. Collaboration among geographically distributed organizations with different goals and cultures provides significant challenges. One experimental question, underlying all that InterMed has set out to achieve, is whether modern 0933-3657/98/$19.00 © 1998 Elsevier Science B.V. All rights reserved. PII S 0 9 3 3 -3 6 5 7 ( 9 7 ) 0 0 0 4 5 -6 12 (1998) 97-123 98 communication technologies can effectively bridge such cultural and geographical gaps, allowing the development of shared visions and cooperative activities so that the end results are greater than any one group could have accomplished on its own. In this paper we summarize the InterMed philosophy and mission, describe our progress over 3 years of collaborative activities, and present study results regarding the nature of the evolving collaborative processes, the perceptions of the participants regarding those processes, and the role that telephone conference calls have played in furthering project goals. Both informal introspection and more formal evaluative work, in which project participants became subjects of study by our evaluation experts from McGill, helped to shift our activities from relatively unfocused to more focused efforts while allowing us to understand the facilitating roles that communications technologies could play in our activities. Our experience and study results suggest that occasional face-to-face meetings are crucial precursors to the effective use of distance communications technologies; that conference calls play an important role in both task-related activities and executive (project management) activities, especially when clarifications are required; and that collaborative productivity is highly dependent upon the gradual development of a shared commitment to a well-defined task that leverages the varying expertise of both local and distant colleagues in the creation of tools of broad utility across the participating sites. E.H. Shortliffe et al. / Artificial Intelligence in Medicin

Machine Learning Approaches for the Prediction of Bone Mineral Density by Using Genomic and Phenotypic Data of 5130 Older Men

The study aimed to utilize machine learning (ML) approaches and genomic data to develop a prediction model for bone mineral density (BMD) and identify the best modeling approach for BMD prediction. The genomic and phenotypic data of Osteoporotic Fractures in Men Study (n = 5130) was analyzed. Genetic risk score (GRS) was calculated from 1103 associated SNPs for each participant after a comprehensive genotype imputation. Data were normalized and divided into a training set (80%) and a validation set (20%) for analysis. Random forest, gradient boosting, neural network, and linear regression were used to develop BMD prediction models separately. Ten-fold cross-validation was used for hyper-parameters optimization. Mean square error and mean absolute error were used to assess model performance. When using GRS and phenotypic covariates as the predictors, all ML models’ performance and linear regression in BMD prediction were similar. However, when replacing GRS with the 1103 individual SNPs in the model, ML models performed significantly better than linear regression (with lasso regularization), and the gradient boosting model performed the best. Our study suggested that ML models, especially gradient boosting, can improve BMD prediction in genomic data

Developing Metadata Categories as a Strategy to Mobilize Computable Biomedical Knowledge

A work by a group of volunteer members drawn from the Mobilizing Computable Biomedical Knowledge community's Standards Workgroup. See mobilizecbk.org for more information about this community and workgroup.Computable biomedical knowledge artifacts (CBKs) are digital objects or entities representing biomedical knowledge as machine-independent data structures that can be parsed and processed by different information systems. The breadth of content represented in CBKs spans all biomedical knowledge related to human health and so it includes knowledge about molecules, cells, organs, individual people, human populations, and the environment. CBKs vary in their scope, purpose, and audience. Some CBKs support biomedical research. Other CBKs help improve health outcomes by enabling clinical decision support, health education, health promotion, and population health analytics. In some instances, CBKs have multiple uses that span research, education, clinical care, or population health. As the number of CBKs grows large, producers must describe them with structured, searchable metadata so that consumers can find, deploy, and use them properly. This report delineates categories of metadata for describing CBKs sufficiently to enable CBKs to be mobilized for various purposes.https://deepblue.lib.umich.edu/bitstream/2027.42/155655/1/MCBK.Metadata.Paper.June2020.f.pdfDescription of MCBK.Metadata.Paper.June2020.f.pdf : MCBK 2020 Virtual Meeting version of Standards Workgroup's Working Paper on CBK Metadat