Analytics Pillar: Transforming Health Interventions
Health intervention research and implementation is one of the biggest users, as well as inspirations, of cutting-edge data science and AI methods. MIDAS collaborates with campus partners to enable the adoption of cutting-edge analytics and modeling of complex data to boost U-M’s biomedical and healthcare research.
Sequential Decision Making for Health Intervention
Overview: MIDAS enables the application of Sequential Decision Making (SDM) to health intervention and other related research through a methodology working group and training for researchers.
The working group aims to develop research grants to both advance cutting-edge SDM methodologies beyond micro-randomized trials / just-in-time adaptive interventions, and to enable the wide adoption of such methodologies. Through a series of meetings, the working group is identifying a) current and developing SDM methodologies that could be enhanced / extended by U-M researchers, b) identifying healthcare challenges that could be mitigated with the application of these methods, and c) identifying challenges in non-health care domain that could benefit from the same methods (such as industrial engineering). .
A workshop, planned for Summer 2023, will focus on methods for optimizing adaptive interventions. This workshop will produce researchers with a strong grasp of the core components of adaptive interventions and available experimental tools for optimizing them, and produce and disseminate via a durable website a set of recommendations on how to teach / interpret the curriculum on optimizing adaptive interventions.
Who Will Benefit: Both SDM methodologists and researchers who may adopt these methods in their research.
Coordinator: Beth Uberseder (Research Manager, MIDAS)
NSF IUCRC : Center for Data-Driven Drug Development and Treatment Assessment (DATA)
Overview: MIDAS researchers and their U-M collaborators and industry and healthcare partners have established an NSF-funded Center for Data-Driven Drug Development and Treatment Assessment (DATA) for pre-competitive research in drug design, drug treatment assessment, drug repositioning, patient phenotyping, and quantitative pharmacovigilance using novel machine learning (ML) and Artificial Intelligence (AI) techniques. The external partners include healthcare systems, non-profits, technology companies and pharmaceutical manufacturers, ranging in size from startups to multinational organizations.
Who Will Benefit: The Center will be a hub to identify and address the complex and time-consuming nature of drug design and treatment assessment, creating solutions that not only reduce the cost associated with the drug design process, but also those that assess, monitor, and optimize treatment and overall patient health.
Coordinator: Kayvan Najarian (PI, DATA; Associate Director, MIDAS; Professor, Computational Medicine and Bioinformatics) and Ivana Tullett (Managing Director, DATA)
Bootcamp on Introductory Data Science for Biomedical Scientists
Overview: The annual bootcamp is open to all U-M researchers and trainees, as well as biomedical scientists from the public and private sectors. We especially welcome faculty members who want to grasp the basic concepts and methods of data science, so that they can start building a data science component in their research, and work more effectively with their data science collaborators. Furthermore, we help like-minded researchers get to know each other through this bootcamp and develop collaboration. Participants will learn how to a) utilize supervised and unsupervised machine learning, including deep learning, for clinical applications; b) determine which data science/artificial intelligence techniques are appropriate for a given clinical application; c) implement these methodologies in Python code, and d) showcase success stories to further exhibit opportunities and approaches to enhance medical research with data science. After the bootcamp, we will offer follow-up sessions that focus on developing data science components for grant proposals, focusing on K grants.
Who Will Benefit: All biomedical scientists who are interested in learning about incorporating data science into their research, but the content is geared towards junior faculty members and those from the public and private sectors.
Coordinator: Kayvan Najarian (PI, DATA; Associate Director, MIDAS; Professor, Computational Medicine and Bioinformatics) and Beth Uberseder (Research Manager, MIDAS)
Wearables Data Platform
Overview: Data from wearable devices are increasingly central to research on health and human behavior, but a lack of standards in data collection and processing will hinder data sharing and the effectiveness of such research. A number of campus organizations, including the Biosciences Initiative, the Exercise and Sports Science Initiative and MIDAS, organized a Wearables Summit in 2021 for U-M researchers, and the key takeaway from the Summit was the urgent need of an effective process for data sharing and workflow management. MIDAS is one of the collaborators in an effort organized by multiple research units and researchers across campus to build a platform to house wearables data at scale and to develop best practices for data collection and sharing.
Who Will Benefit: This potential offering to the U-M community can facilitate collaboration and data sharing among researchers who use internal and external wearables data collected in various studies.
Coordinator: Sean Meyer (Senior Scientist, MIDAS)