Girish N. Nadkarni

发表

Heterogeneous Graph Embeddings of Electronic Health Records Improve Critical Care Disease Predictions

Riccardo Miotto, Tingyi Wanyan, Martin Kang, 2020, AIME.

Sleep in the Natural Environment: A Pilot Study

Riccardo Miotto, Jessica K. De Freitas, Fayzan F. Chaudhry, 2020, Sensors.

Leveraging hierarchy in medical codes for predictive modeling

Girish N. Nadkarni, John V. Guttag, Anima Singh, 2014, BCB.

Incorporating temporal EHR data in predictive models for risk stratification of renal function deterioration

Girish N. Nadkarni, John V. Guttag, Anima Singh, 2014, J. Biomed. Informatics.

Disease progression subtype discovery from longitudinal EMR data with a majority of missing values and unknown initial time points

Girish N. Nadkarni, Ilkka Huopaniemi, Vaneet Lotay, 2014, AMIA.

Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

Marinka Zitnik, Riccardo Miotto, Tingyi Wanyan, 2021, Patterns.

Development and validation of an electronic phenotyping algorithm for chronic kidney disease

George Hripcsak, Girish N. Nadkarni, Chunhua Weng, 2014, AMIA.

A Genome- and Phenome- Wide Study of Diverticulosis

Girish N. Nadkarni, Joshua C. Denny, Jyotishman Pathak, 2015, AMIA.

Augmented Intelligence with Natural Language Processing Applied to Electronic Health Records is Useful for Identifying Patients with Non-Alcoholic Fatty Liver Disease at Risk for Disease Progression

Joseph L. Kannry, Girish N. Nadkarni, Catherine K. Craven, 2019, bioRxiv.

Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit

Riccardo Miotto, Tingyi Wanyan, Akhil Vaid, 2021, IEEE Transactions on Big Data.

Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records

Riccardo Miotto, Kipp W. Johnson, Jessica K. De Freitas, 2021, Patterns.

Translational ResearchLocation-Specific Oral Microbiome Possesses Features Associated With Chronic Kidney Disease

Girish N. Nadkarni, Inga Peter, Ruiqi Huang, 2017 .