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Musculoskeletal, Informatics and Methodological Core

The Musculoskeletal, Informatics and Methodological (MIM) Core offers support with computable phenotyping, clinical trial design and statistical analyses. This group connects investigators and facilitates multidisciplinary research that leverages the use of novel technologies and resources to improve musculoskeletal health.

This core will support study design, statistical efforts, and informatics analysis of all Indiana Center for Musculoskeletal Health clinical research.

The MIM Core will also provide consultation with investigators seeking advanced data science and informatics methodology to develop appropriate hypotheses, design studies, and identify collaborators, leveraging the extensive data science, informatics, genomics, biostatistics and machine learning expertise on campus.

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flow of work in the core. A green arrow moving to the center shows work starting with the clinical researcher or basic scientists. They use the FIT core database to identify specimens or data. Then they use the Data Mart to obtain more clinical information. A blue arrow moving from the right into the middle shows an epidemiologist using the Data Mart to identify a cohort to test hypothesis and measure outcomes. Then they may identify individuals with biospecimens from the FIT core to identify mechanism of musculoskeletal health.

Goals of the core

Training

Expand the abilities of researchers by training cross-disciplinary collaborative teams in the use and application of novel tools, methodologies, designs and statistical analyses.


Research

Enhance clinical research by leveraging large databases to define and validate relevant computer musculoskeletal phenotypes (computer algorithms to identify particular characteristics of outcomes).


Clinical Trials

This core interacts with investigators and members of thematic research teams to accelerate the human to bench and back cycle of discovery through the characterization of genotypes and molecular musculoskeletal phenotypes.



Related Publications

Luo X, Ding H, Broyles A, Warden SJ, Moorthi RN, Imel EA. Using machine learning to detect sarcopenia from electronic health records. DIGITAL HEALTH. 2023;9.



Learn more about the Musculoskeletal Data Mart