Perotte, Adler

Characterizing treatment pathways at scale using the OHDSI network

Hripcsak, G., Ryan, P. B., Duke, J. D., Shah, N. H., Park, R. W., Huser, V., Suchard, M. A., Schuemie, M. J., DeFalco, F. J., Perotte, A., et al. (2016) · Proceedings of the National Academy of Sciences

Medical Coding and Health InformationMachine Learning in Healthcare

Cited by 326

Deep Learning Electrocardiographic Analysis for Detection of Left-Sided Valvular Heart Disease

Elias, P., Poterucha, T. J., Rajaram, V., Moller, L. M., Rodriguez, V., Bhave, S., Hahn, R. T., Tison, G., Abreau, S. A., Barrios, J., et al. (2022) · Journal of the American College of Cardiology

ECG Monitoring and AnalysisArtificial Intelligence in Healthcare

Cited by 153

Translating evidence into practice: eligibility criteria fail to eliminate clinically significant differences between real-world and study populations

Averitt, A. J., Weng, C., Ryan, P., & Perotte, A. (2020) · npj Digital Medicine

Advanced Causal Inference TechniquesMeta-analysis and systematic reviews

Cited by 137

Risk prediction for chronic kidney disease progression using heterogeneous electronic health record data and time series analysis

Perotte, A., Ranganath, R., Hirsch, J. S., Blei, D., & Elhadad, N. (2015) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareChronic Kidney Disease and Diabetes

Cited by 128

Electrocardiographic deep learning for predicting post-procedural mortality: a model development and validation study

Ouyang, D., Theurer, J., Stein, N. R., Hughes, J. W., Elias, P., He, B., Yuan, N., Duffy, G., Sandhu, R. K., Ebinger, J., et al. (2024) · The Lancet Digital Health

ECG Monitoring and AnalysisCardiac, Anesthesia and Surgical Outcomes

Cited by 64

Deep learning to detect left ventricular structural abnormalities in chest X-rays

Bhave, S., Rodriguez, V., Poterucha, T., Mutasa, S., Aberle, D., Capaccione, K. M., Chen, Y., Dsouza, B., Dumeer, S., Goldstein, J., et al. (2024) · European Heart Journal

COVID-19 diagnosis using AIArtificial Intelligence in Healthcare and Education

Cited by 38

Utilizing timestamps of longitudinal electronic health record data to classify clinical deterioration events

Fu, L.-H., Knaplund, C., Cato, K., Perotte, A., Kang, M.-J., Dykes, P. C., Albers, D., & Collins Rossetti, S. (2021) · Journal of the American Medical Informatics Association

Machine Learning in HealthcareHealthcare Technology and Patient Monitoring

Cited by 14

EvidenceMap: a three-level knowledge representation for medical evidence computation and comprehension

Kang, T., Sun, Y., Kim, J. H., Ta, C., Perotte, A., Schiffer, K., Wu, M., Zhao, Y., Moustafa-Fahmy, N., Peng, Y., et al. (2023) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesMeta-analysis and systematic reviews

Cited by 14

A neuro-symbolic method for understanding free-text medical evidence.

Kang, T., Turfah, A., Kim, J., Perotte, A., & Weng, C. (2021) · Journal of the American Medical Informatics Association

Biomedical Text Mining and OntologiesTopic Modeling

Cited by 11