Reeves, Ruth

A clinically applicable approach to continuous prediction of future acute kidney injury

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Nature

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 1,086

Adaptation of an NLP system to a new healthcare environment to identify social determinants of health.

Reeves, R. M., Christensen, L., Brown, J. R., Conway, M., Levis, M., Gobbel, G. T., Shah, R. U., Goodrich, C., Ricket, I., Minter, F., et al. (2021) · Journal of Biomedical Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 45

Representing and utilizing clinical textual data for real world studies: An OHDSI approach

Keloth, V. K., Banda, J. M., Gurley, M., Heider, P. M., Kennedy, G., Liu, H., Liu, F., Miller, T., Natarajan, K., V Patterson, O., et al. (2023) · Journal of Biomedical Informatics

Biomedical Text Mining and OntologiesMachine Learning in Healthcare

Cited by 16

Developing Deep Learning Continuous Risk Models for Early Adverse Event Prediction in Electronic Health Records: an AKI Case Study

Tomašev, N., Glorot, X., Rae, J. W., Zielinski, M., Askham, H., Saraiva, A., Mottram, A., Meyer, C., Ravuri, S., Protsyuk, I., et al. (2019) · Research Square

Machine Learning in HealthcareAcute Kidney Injury Research

Cited by 3