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Martina Mueller PhD

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  • Professor
  • College of Nursing
  • Department of Nursing
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Dr. Martina Mueller is a Professor of Biostatistics in the College of Nursing with a joint appointment in the Department of Public Health Sciences (DPHS) at the Medical University of South Carolina (MUSC). Dr Mueller received a degree in nursing (Germany) in 1985, and was actively engaged in the field as an emergency as well as operating room nurse for over 6 years.  Because of her additional interest in the area of informatics as applied to health care, she completed an MS degree in medical informatics in 1996.  Following this she was awarded a Fulbright Scholarship to continue her informatics studies in the Department of Biostatistics, Bioinformatics, and Epidemiology (DBBE now Department of Public Health Sciences) at MUSC where she subsequently completed the Ph.D. with areas of emphasis in bioinformatics and biostatistics.

Following completion of her postdoctoral training, she was appointed as a Research Assistant Professor in DBBE at MUSC in which she collaborated with MUSC researchers on several major ongoing NIH and VA funded projects, and provided statistical support for the Center of Health Disparities Research. Dr Mueller also served as Associate Director of the Data Coordinating Center for the Consortium of Research on ECT (CORE) which has conducted several large NIMH-sponsored multi-center clinical trials. She currently serves as the site PI and Director of the Statistical and Data Center for the ECT-AD study, a multi-center study investigating the use of electroconvulsive therapy to treat severe agitation in hospitalized patients with dementia. Responsibilities include oversight of data management and data quality control, participation in development and execution of the data analysis plan, development of trial reports for NIH, FDA and DSMB, and interpretation and presentation of trial results.

In addition to her collaborative work, Dr. Mueller has pursued independent research related to the development of informatics tools to assist in the care of premature infants in neonatal intensive care units (NICU).  Her research, an outgrowth of her Ph.D. dissertation research, involved application of neural network methodology to assist inexperienced NICU physicians and nurses in predicting extubation outcome in these fragile infants managed on mechanical ventilators.