Giannini, Heather M.

CD8+ T cells contribute to survival in patients with COVID-19 and hematologic cancer

Bange, E. M., Han, N. A., Wileyto, P., Kim, J. Y., Gouma, S., Robinson, J., Greenplate, A. R., Hwee, M. A., Porterfield, F., Owoyemi, O., et al. (2021) · Nature Medicine

COVID-19 Clinical Research StudiesSARS-CoV-2 and COVID-19 Research

Cited by 519

A Machine Learning Algorithm to Predict Severe Sepsis and Septic Shock: Development, Implementation, and Impact on Clinical Practice*

Giannini, H. M., Ginestra, J. C., Chivers, C., Draugelis, M., Hanish, A., Schweickert, W. D., Fuchs, B. D., Meadows, L., Lynch, M., Donnelly, P. J., et al. (2019) · Critical Care Medicine

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 269

Activation of tumor suppressor protein PP2A inhibits KRAS-driven tumor growth

Sangodkar, J., Perl, A., Tohme, R., Kiselar, J., Kastrinsky, D. B., Zaware, N., Izadmehr, S., Mazhar, S., Wiredja, D. D., O’Connor, C. M., et al. (2017) · Journal of Clinical Investigation

Protein Tyrosine PhosphatasesProtein Kinase Regulation and GTPase Signaling

Cited by 196

Clinician perception of a machine learning–based early warning system designed to predict severe sepsis and septic shock

Ginestra, J. C., Giannini, H. M., Schweickert, W. D., Meadows, L., Lynch, M. J., Pavan, K., Chivers, C. J., Draugelis, M., Donnelly, P. J., Fuchs, B. D., et al. (2019) · Critical Care Medicine

Sepsis Diagnosis and TreatmentHealthcare Technology and Patient Monitoring

Cited by 159