Precision Health member Lana Garmire, PhD is an Associate Professor of Biostatistics and Associate Professor of Computational Medicine & Bioinformatics at the University of Michigan School of Public Health. Dr. Garmire is an awardee of US Presidential Early Career Scientists and Engineers in 2019, the highest honor bestowed to the most outstanding early career scientists and engineers in the United States.

Before joining University of Michigan DCMB department, she rapidly rose to tenure (Dec. 2012 to Jun. 2017) at University of Hawaii Cancer Center, and has become a nationally and internationally recognizable translational bioinformatics scientist leading a multidisciplinary team of computational and experimental human genomics. She has mentored over 40 MD fellows, postdocs, graduate students and undergraduates of various academic backgrounds, in Biology, Mathematics, Phyiscs, (bio)Statistics, Bioengineering, Computer Science and Electrical Engineering. She has served on various NIH study sections. She is an Associate Editor of BMC Bioinformatics and Guest Editor of PLoS Computational Biology.

• Tell us a bit more about the details of your current research/projects

We are currently using U-M Precision Health data to predict the time of delivery for preeclampsia, a pregnancy disease with the clinical symptoms of high blood pressure and proteins in the urine or multiple-organ damages. This disease currently has no cure except delivering the babies and stop pregnancy (which usually mean pre-term deliveries)

• What is innovative/new/exciting about these projects?

This project is innovative since it is the first of its kind to predict precisely when the babies need to be delivered since the diagnosis of this disease.

•  What is the anticipated outcome of this research?

We expect that this work will much better inform both the OBGYN and the patient, to prepare them better for delivery time; it also identified various risk factors for short delivery time.

• How will it benefit patients and clinicians?

One of the biggest problems of dealing with preeclampsia is the “unknowns”: we do not know which patient’s conditions will deteriorate quickly and thus requires prompt medical procedures. Currently, in the developed countries, the patients are closely monitored by the OBGYN; however, the risks of fatality is much higher in under-developed countries without sufficient resources.

• How is Precision Health supporting this research?

We obtained the EMR data from Precision Health’s Data Direct; we got a lot of help from the Data Analytics group, particularly in retrieving the accurate information of gestational ages at the time of diagnosis, and at the time of delivery. Such information made this study one of the kind.

• What are your research interests, broadly?

I’m a biomedical data scientist interested in precision medicine. I use the data, either genomics, or EMR data, to help us predict patient outcome, or predict new drugs for treating diseases.

• How does your work apply to the field of precision health?

We apply to various diseases under precision health, for example, finding better ways to manage and predict women’s pregnancy complications, and reduce healthcare burdens.

• Please share some of your recent/significant work:

Deep learning-based prognosis models accurately predict the time to delivery among preeclamptic pregnancies using electronic health record | medRxiv;

Building and validating 5-feature models to predict preeclampsia onset time from electronic health record data | medRxiv

• What do you like to do when you aren’t doing research?

I tweet about science and other life matters from @GarmireGroup; I also like to play with my kids to outdoors and travel with my family; I hope to be an amateur farmer one day.