The accumulation of health data with unprecedented speed and volume brings unprecedented opportunities and challenges to improve healthcare. On the most microscopic level, we are now examining the genome one cell at a time, monitoring individuals’ health behavior in real-time, and prescribing medication based on the genetics and chemistry of each individual. On the most macroscopic level, we can combine dozens of various clinical record data for one patient, and can analyze the health data of an entire nation. However, our ability to gain insight into such vast data lags significantly behind our ability to accumulate data. The Center for Data-Intensive Health Science Research at MIDAS aims to enhance U-M biomedical researchers’ ability embrace the opportunities and challenges brought about by advances in data science. The center will catalyze the development of theories and innovative methodologies in data science, and their application to the entire spectrum of biomedical science, from basic research to translation research, to clinical applications, with the ultimate goal of using data science to improve the health of our society.
Through our Challenge Initiatives, the Center for Data-Intensive Health Science Research at MIDAS is funding three initial projects in data science methodology development and application. “Michigan Center for Single-Cell Genomic Data Analytics”, led by Jun Li of the U-M Medical School, will develop state-of-the-art methodologies in single-cell genomics that can be applied to a large number of research fields in biology. “From Big Data to Vital Insights: Michigan Center for Health Analytics & Medical Prediction (M-CHAMP),” led by Brahmajee K. Nallamothu of the U-M Medical School, will develop innovative analytic techniques to examine temporal patterns in complex patient data to improve the early diagnosis and treatment of a variety of health conditions. “Identifying Real-Time Data Predictors of Stress and Depression Using Mobile Technology”, led by Srijan Sen of the U-M Medical School, will utilize multi-modal mobile and wearable data to develop personalized models of depression onset under stress by examining the interactions of physiology, behavior and environment.
With these projects as the initial core research, the Center for Data-Intensive Health Science Research will focus on:
- Disseminating tools and methods, as soon as they become available, to empower campus-wide health research that integrates data science.
- Building a collaborative network of Big Data health researchers that will propel UM to the forefront of data-intensive health research in the nation.
- Forming industry partnerships and transform research findings into clinical applications that have immediate impact on healthcare.
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