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Author Details
Full Name
Riccardo Miotto
Affiliation
Icahn School of Medicine at Mount Sinai
ORCID
Career Start Year
2013
Papers
40
H Index
20
Expertise
CM4AI Collaborator
Ying Ding (CM4AI)
PMID
Paper Title
Journal Title
Published Year
36735512
Deep Learning in Medicine.
Clin J Am Soc Nephrol
2023
36735512
Deep Learning in Medicine.
Clin J Am Soc Nephrol
2023
36310683
Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation.
Cardiovasc Digit Health J
2022
36310683
Enhancing convolutional neural network predictions of electrocardiograms with left ventricular dysfunction using a novel sub-waveform representation.
Cardiovasc Digit Health J
2022
36448021
Predicting hypertension onset from longitudinal electronic health records with deep learning.
JAMIA Open
2022
36448021
Predicting hypertension onset from longitudinal electronic health records with deep learning.
JAMIA Open
2022
33442560
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients.
ArXiv
2021
33529156
Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.
J Med Internet Res
2021
33768136
Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.
IEEE Trans Big Data
2021
33442560
Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients.
ArXiv
2021
33400679
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.
JMIR Med Inform
2021
34553174
Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.
Patterns (N Y)
2021
33768136
Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit.
IEEE Trans Big Data
2021
33529156
Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.
J Med Internet Res
2021
34553174
Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.
Patterns (N Y)
2021
33400679
Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.
JMIR Med Inform
2021
32130159
Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study.
JMIR Med Inform
2020
32138289
Sleep in the Natural Environment: A Pilot Study.
Sensors (Basel)
2020
31797613
Scaling structural learning with NO-BEARS to infer causal transcriptome networks.
Pac Symp Biocomput
2020
32130159
Identifying Acute Low Back Pain Episodes in Primary Care Practice From Clinical Notes: Observational Study.
JMIR Med Inform
2020
32495652
Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype.
J Cardiovasc Pharmacol Ther
2020
32817979
Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19.
medRxiv
2020
32594164
Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City.
Clin Infect Dis
2020
32699826
Deep representation learning of electronic health records to unlock patient stratification at scale.
NPJ Digit Med
2020
33247020
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.
BMJ Open
2020
33027032
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
J Med Internet Res
2020
32511655
Clinical Characteristics of Hospitalized Covid-19 Patients in New York City.
medRxiv
2020
32511564
Acute Kidney Injury in Hospitalized Patients with COVID-19.
medRxiv
2020
31797613
Scaling structural learning with NO-BEARS to infer causal transcriptome networks.
Pac Symp Biocomput
2020
33027032
Machine Learning to Predict Mortality and Critical Events in a Cohort of Patients With COVID-19 in New York City: Model Development and Validation.
J Med Internet Res
2020
33247020
Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.
BMJ Open
2020
32594164
Coronavirus 2019 and People Living With Human Immunodeficiency Virus: Outcomes for Hospitalized Patients in New York City.
Clin Infect Dis
2020
32817979
Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19.
medRxiv
2020
32699826
Deep representation learning of electronic health records to unlock patient stratification at scale.
NPJ Digit Med
2020
32495652
Machine Learning in Cardiology-Ensuring Clinical Impact Lives Up to the Hype.
J Cardiovasc Pharmacol Ther
2020
32138289
Sleep in the Natural Environment: A Pilot Study.
Sensors (Basel)
2020
32511655
Clinical Characteristics of Hospitalized Covid-19 Patients in New York City.
medRxiv
2020
32511564
Acute Kidney Injury in Hospitalized Patients with COVID-19.
medRxiv
2020
31066697
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.
JMIR Med Inform
2019
31214700
PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.
Bioinformatics
2019
31066697
Natural Language Processing of Clinical Notes on Chronic Diseases: Systematic Review.
JMIR Med Inform
2019
31214700
PatientExploreR: an extensible application for dynamic visualization of patient clinical history from electronic health records in the OMOP common data model.
Bioinformatics
2019
29218877
Automated disease cohort selection using word embeddings from Electronic Health Records.
Pac Symp Biocomput
2018
28481991
Deep learning for healthcare: review, opportunities and challenges.
Brief Bioinform
2018
29036387
Uncovering exposures responsible for birth season - disease effects: a global study.
J Am Med Inform Assoc
2018
29880128
Artificial Intelligence in Cardiology.
J Am Coll Cardiol
2018
30474079
Trends in anesthesiology research: a machine learning approach to theme discovery and summarization.
JAMIA Open
2018
29218877
Automated disease cohort selection using word embeddings from Electronic Health Records.
Pac Symp Biocomput
2018
31304340
Reflecting health: smart mirrors for personalized medicine.
NPJ Digit Med
2018
28200013
Systematic analyses of drugs and disease indications in RepurposeDB reveal pharmacological, biological and epidemiological factors influencing drug repositioning.
Brief Bioinform
2018
1 - 50 of 80
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row(s) 1 - 30 of 30
Collaborators
Benjamin S Glicksberg
Hasso Plattner Institute for Digital Health, Icahn School of Medicine at Mount Sinai
Co-authored papers
21
Joel T Dudley
Institute for Next Generation Healthcare
Co-authored papers
17
Girish N Nadkarni
Icahn School of Medicine at Mount Sinai
Co-authored papers
12
Kipp W Johnson
Co-authored papers
12
Sulaiman Somani
Icahn School of Medicine at Mount Sinai
Co-authored papers
11
Erwin P Bottinger
Hasso Plattner Institute for Digital Health at Mount Sinai
Co-authored papers
10
Matteo Danieletto
Co-authored papers
9
Zahi A Fayad
Co-authored papers
8
Alexander W Charney
Icahn School of Medicine at Mount Sinai
Co-authored papers
8
Chunhua Weng
Columbia University Irving Medical Center
Co-authored papers
8
Jessica K De Freitas
Icahn School of Medicine at Mount Sinai
Co-authored papers
8
Khader Shameer
School of Public Health, Imperial College London
Co-authored papers
6
Li Li
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Brian A Kidd
Co-authored papers
4
Mary Regina Boland
University of Pennsylvania
Co-authored papers
4
Valentin Fuster
Co-authored papers
4
Nicholas P Tatonetti
Columbia Universtiy College of Physicians and Surgeons
Co-authored papers
4
Allan C Just
Co-authored papers
4
Carol R Horowitz
Institute for Health Equity Research, Icahn School of Medicine at Mount Sinai
Co-authored papers
4
Patricia Kovatch
Co-authored papers
4
Hao-Chih Lee
Co-authored papers
3
Marcus A Badgeley
Co-authored papers
3
Joseph Finkelstein
Co-authored papers
3
Fei Wang
Peking University Cancer Hospital & Institute, Beijing Cancer Hospital & Institute
Co-authored papers
3
Rong Chen
Co-authored papers
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Eric E Schadt
Icahn School of Medicine at Mount Sinai
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