Marcus A. Badgeley

发表

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

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

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..

Controlling Level of Unconsciousness with Deep Reinforcement Learning

Emery N. Brown, Marcus A. Badgeley, Gabe Schamberg, 2020 .

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..

Quantification of BERT Diagnosis Generalizability Across Medical Specialties Using Semantic Dataset Distance pdf

Marcus A. Badgeley, Juan Garcia, William Su, 2020, AMIA ... Annual Symposium proceedings. AMIA Symposium.

Deep learning predicts hip fracture using confounding patient and healthcare variables

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

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..

Wide and deep volumetric residual networks for volumetric image classification pdf

Marcus A. Badgeley, Anthony B. Costa, Eric K. Oermann, 2017, ArXiv.

Hybrid Bayesian-rank integration approach improves the predictive power of genomic dataset aggregation

Marcus A. Badgeley, Stuart C. Sealfon, Maria D. Chikina, 2015, Bioinform..

Confounding variables can degrade generalization performance of radiological deep learning models pdf

Marcus A. Badgeley, Anthony B. Costa, Eric K. Oermann, 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..

Constructing a control-ready model of EEG signal during general anesthesia in humans.

Emery N. Brown, Marcus A. Badgeley, Patrick L. Purdon, 2019 .

Continuous action deep reinforcement learning for propofol dosing during general anesthesia

Emery N. Brown, Marcus A. Badgeley, Gabriel Schamberg, 2021, Artif. Intell. Medicine.

Automated deep-neural-network surveillance of cranial images for acute neurologic events

Marcus A. Badgeley, Jun S. Kim, Samuel K. Cho, 2018, Nature Medicine.

Variable generalization performance of a deep learning model to detect pneumonia in chest radiographs: A cross-sectional study

Marcus A. Badgeley, J. Zech, M. Badgeley, 2018, PLoS medicine.

Pleiotropic Variability Score: A Genome Interpretation Metric to Quantify Phenomic Associations of Genomic Variants

Marcus A. Badgeley, Kipp W. Johnson, J. Dudley, 2021 .

Automated deep-neural-network surveillance of cranial images for acute neurologic events

Marcus A. Badgeley, J. Lehár, J. Zech, 2018, Nature Medicine.

Using deep learning to identify bladder cancers with FGFR‐activating mutations from histology images

Marcus A. Badgeley, Y. Lo, Constantine S Velmahos, 2021, Cancer medicine.

In Vivo Targeting of Inflammation-Associated Myeloid-Related Protein 8/14 Via Gadolinium Immunonanoparticles

Marcus A. Badgeley, S. Rajagopalan, M. Badgeley, 2012, Arteriosclerosis, thrombosis, and vascular biology.

Facile Cholesterol Loading with a New Probe ezFlux Allows for Streamlined Cholesterol Efflux Assays

Marcus A. Badgeley, S. Rajagopalan, M. Badgeley, 2020, ACS omega.

EHDViz: clinical dashboard development using open-source technologies

Marcus A. Badgeley, Khader Shameer, Max S. Tomlinson, 2016, BMJ Open.

No effect of acute exposure to coarse particulate matter air pollution in a rural location on high-density lipoprotein function

Marcus A. Badgeley, R. Brook, R. Bard, 2014, Inhalation toxicology.

Using deep learning algorithms to simultaneously identify right and left ventricular dysfunction from the electrocardiogram.

Marcus A. Badgeley, Kipp W. Johnson, Benjamin S. Glicksberg, 2021, medRxiv.

Hybrid nanoparticles improve targeting to inflammatory macrophages through phagocytic signals

Marcus A. Badgeley, S. Rajagopalan, M. Badgeley, 2015, Journal of controlled release : official journal of the Controlled Release Society.

Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

Marcus A. Badgeley, Kipp W. Johnson, Benjamin S. Glicksberg, 2019, BMC Medical Genomics.

Natural Language-based Machine Learning Models for the Annotation of Clinical Radiology Reports.

Marcus A. Badgeley, J. Lehár, J. Zech, 2018, Radiology.

PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model

Theodore C Goldstein, Riccardo Miotto, Marcus A. Badgeley, 2019, Bioinform..

Epidemiology of 10,000 high school football injuries: patterns of injury by position played.

Marcus A. Badgeley, R. Comstock, S. Fields, 2013, Journal of physical activity & health.

Machine learning of EEG spectra classifies unconsciousness during GABAergic anesthesia

Marcus A. Badgeley, John H. Abel, David W. Zhou, 2021, PloS one.

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

Marcus A. Badgeley, Joel T Dudley, Pei Wang, 2017, Nature Biotechnology.

Deep learning guided stroke management: a review of clinical applications

Marcus A. Badgeley, J. Mocco, M. Badgeley, 2017, Journal of NeuroInterventional Surgery.

Correction to: Integrative analysis of loss-of-function variants in clinical and genomic data reveals novel genes associated with cardiovascular traits

Marcus A. Badgeley, Kipp W. Johnson, E. Schadt, 2019, BMC Medical Genomics.

The Asthma Mobile Health Study, a large-scale clinical observational study using ResearchKit

Marcus A. Badgeley, Erick R. Scott, Rosalind J Wright, 2017, Nature Biotechnology.

Controlling Level of Unconsciousness by Titrating Propofol with Deep Reinforcement Learning

Emery N. Brown, Marcus A. Badgeley, Gabe Schamberg, 2020, AIME.

Pharmacogenomic insights in psychiatric care: uncovering novel actionability, allele-specific CYP2D6 copy number variation, and phenoconversion in 15,000 patients.

Marcus A. Badgeley, Kathleen C. Barnes, Francisco M. De La Vega, 2024, Molecular psychiatry.