Benjamin S. Glicksberg

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.

Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning

Rong Chen, Riccardo Miotto, Marcus A. Badgeley, 2017, Briefings Bioinform..

ROMOP: a light-weight R package for interfacing with OMOP-formatted electronic health record data

Benjamin S. Glicksberg, Atul J. Butte, Nicholas P. Tatonetti, 2019, JAMIA open.

Loss-of-function of neuroplasticity-related genes confers risk for human neurodevelopmental disorders

Li Li, Rong Chen, Benjamin S. Glicksberg, 2018, PSB.

An Integrative Pipeline for Multi-Modal Discovery of Disease Relationships

Li Li, Rong Chen, Benjamin S. Glicksberg, 2014, Pacific Symposium on Biocomputing.

Prioritizing Small Molecule as Candidates for Drug Repositioning using Machine Learning

Kipp W. Johnson, Benjamin S. Glicksberg, Joel Dudley, 2018, bioRxiv.

Identify Cancer Driver Genes Through Shared Mendelian Disease Pathogenic Variants and Cancer Somatic Mutations

Rong Chen, Benjamin S. Glicksberg, Meng Ma, 2017, PSB.

CANDI: an R package and Shiny app for annotating radiographs and evaluating computer-aided diagnosis

Marcus A. Badgeley, Benjamin S. Glicksberg, Joel Dudley, 2018, Bioinform..

fragilityindex: An R Package for Statistical Fragility Estimates in Biomedicine

Kipp W. Johnson, Benjamin S. Glicksberg, Khader Shameer, 2019, bioRxiv.

Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

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

Automatic processing of Electronic Medical Records using Deep Learning

Oscar Mayora-Ibarra, Benjamin S. Glicksberg, Joel Dudley, 2018, PervasiveHealth.

OCTAD: an open workspace for virtually screening therapeutics targeting precise cancer patient groups using gene expression features

Benjamin S. Glicksberg, Ke Liu, Jing Xing, 2019, Nature Protocols.

Deep learning predicts hip fracture using confounding patient and healthcare variables

Marcus A. Badgeley, Benjamin S. Glicksberg, Bethany Percha, 2018, npj Digital Medicine.

Causal inference on electronic health records to assess blood pressure treatment targets: An application of the parametric g formula

Kipp W. Johnson, Benjamin S. Glicksberg, Joel Dudley, 2018, PSB.

Deep Learning with Heterogeneous Graph Embeddings for Mortality Prediction from Electronic Health Records pdf

Tingyi Wanyan, Ariful Azad, Ying Ding, 2020, Data Intelligence.

Robust prediction of clinical outcomes using cytometry data

Benjamin S. Glicksberg, Atul J. Butte, Zicheng Hu, 2018, Bioinform..

Time Aggregation and Model Interpretation for Deep Multivariate Longitudinal Patient Outcome Forecasting Systems in Chronic Ambulatory Care pdf

Benjamin S. Glicksberg, Atul J. Butte, Beau Norgeot, 2018, ArXiv.

Translational bioinformatics in the era of real-time biomedical, health care and wellness data streams

Riccardo Miotto, Marcus A. Badgeley, Benjamin S. Glicksberg, 2016, Briefings Bioinform..

Evaluation of patient re-identification using laboratory test orders and mitigation via latent space variables

Kipp W. Johnson, Jessica K. De Freitas, Benjamin S. Glicksberg, 2018, PSB.

Processing of Electronic Health Records using Deep Learning: A review pdf

Oscar Mayora-Ibarra, Benjamin S. Glicksberg, Joel Dudley, 2018, ArXiv.

Comparative analyses of population-scale phenomic data in electronic medical records reveal race-specific disease networks

Li Li, Rong Chen, Marcus A. Badgeley, 2016, Bioinform..

Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining

Li Li, Kipp W. Johnson, Benjamin S. Glicksberg, 2018, BMC Medical Informatics and Decision Making.

Predicting age by mining electronic medical records with deep learning characterizes differences between chronological and physiological age

Benjamin S. Glicksberg, Joel Dudley, Li Li, 2017, J. Biomed. Informatics.

Deep representation learning of electronic health records to unlock patient stratification at scale

Cesare Furlanello, Riccardo Miotto, Benjamin S. Glicksberg, 2020, npj Digital Medicine.

Automated disease cohort selection using word embeddings from Electronic Health Records

Li Li, Rong Chen, Riccardo Miotto, 2018, PSB.

Development and clinical application of an integrative genomic approach to personalized cancer therapy

Benjamin S. Glicksberg, Gintaras Deikus, Imane Bourzgui, 2016, Genome Medicine.