Digital Commons Network^™

Going Remote: Implementing Digital Research Methods At An Academic Medical Center During Covid-19, Katie E Keenoy, Eric J Lenze, Ginger E Nicol

2020-Current year OA Pubs

COVID-19 has forced medical research institutions to conduct clinical research remotely. Here, we describe how a university's mHealth Research Core helped facilitate the shift to remote research during the COVID-19 pandemic. In 2019 (pre-pandemic), we conducted stakeholder interviews and leadership group sessions to identify, create, and implement resources and core functions to support investigator-initiated mHealth research. Between April 2019 and February 2020, we identified four investigator needs: 1) a

Perceived Barriers To Assessing Understanding And Appreciation Of Informed Consent In Clinical Trials: A Mixed-Method Study, Erin D Solomon, Jessica Mozersky, Kari Baldwin, Matthew P Wroblewski, Meredith V Parsons, Melody Goodman, James M Dubois

2020-Current year OA Pubs

INTRODUCTION: Participants and research professionals often overestimate how well participants understand and appreciate consent information for clinical trials, and experts often vary in their determinations of participant's capacity to consent to research. Past research has developed and validated instruments designed to assess participant understanding and appreciation, but the frequency with which they are utilized is unknown.

METHODS: We administered a survey to clinical researchers working with older adults or those at risk of cognitive impairment (

RESULTS: We found that using a validated assessment of consent is relatively uncommon, being used by only 44% of researchers who had an opportunity. …

Machine Learning Applications In Microbial Ecology, Human Microbiome Studies, And Environmental Monitoring, Ryan B. Ghannam, Stephen Techtmann

Michigan Tech Publications, Part 1

Advances in nucleic acid sequencing technology have enabled expansion of our ability to profile microbial diversity. These large datasets of taxonomic and functional diversity are key to better understanding microbial ecology. Machine learning has proven to be a useful approach for analyzing microbial community data and making predictions about outcomes including human and environmental health. Machine learning applied to microbial community profiles has been used to predict disease states in human health, environmental quality and presence of contamination in the environment, and as trace evidence in forensics. Machine learning has appeal as a powerful tool that can provide deep insights …

Teaching For Implementation: A Framework For Building Implementation Research And Practice Capacity Within The Translational Science Workforce, Aaron L Leppin, Ana A Baumann, Maria E Fernandez, Brittany N Rudd, Kathleen R Stevens, David O Warner, Bethany M Kwan, Rachel C Shelton

2020-Current year OA Pubs

Implementation science offers a compelling value proposition to translational science. As such, many translational science stakeholders are seeking to recruit, teach, and train an implementation science workforce. The type of workforce that will make implementation happen consists of both implementation researchers and practitioners, yet little guidance exists on how to train such a workforce. We-members of the Advancing Dissemination and Implementation Sciences in CTSAs Working Group-present the Teaching For Implementation Framework to address this gap. We describe the differences between implementation researchers and practitioners and demonstrate what and how to teach them individually and in co-learning opportunities. We briefly comment …

Accurate Diagnosis Of Colorectal Cancer Based On Histopathology Images Using Artificial Intelligence, K. S. Wang, G. Yu, C. Xu, X. H. Meng, J. Zhou, W. Zhou, Et. Al.

Michigan Tech Publications, Part 1

Background: Accurate and robust pathological image analysis for colorectal cancer (CRC) diagnosis is time-consuming and knowledge-intensive, but is essential for CRC patients’ treatment. The current heavy workload of pathologists in clinics/hospitals may easily lead to unconscious misdiagnosis of CRC based on daily image analyses. Methods: Based on a state-of-the-art transfer-learned deep convolutional neural network in artificial intelligence (AI), we proposed a novel patch aggregation strategy for clinic CRC diagnosis using weakly labeled pathological whole-slide image (WSI) patches. This approach was trained and validated using an unprecedented and enormously large number of 170,099 patches, > 14,680 WSIs, from > 9631 subjects that covered …