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Riccardo Miotto

Icahn School of Medicine at Mount Sinai
University of Padua

81

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3648

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Unsupervised Learning to Subphenotype Heart Failure Patients from Electronic Health Records

2021 ICAI | International Conference on Artificial Intelligence
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Active learning for medical code assignment

2021 arXiv: Learning
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Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

2021 arXiv: Learning
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Phe2vec: Automated disease phenotyping based on unsupervised embeddings from electronic health records.

2021
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Use of Physiological Data From a Wearable Device to Identify SARS-CoV-2 Infection and Symptoms and Predict COVID-19 Diagnosis: Observational Study.

2021 Journal of Medical Internet Research
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Contrastive Learning Improves Critical Event Prediction in COVID-19 Patients

2021
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Domain-Specific Event Abstraction

2021 BIS | Business Information Systems
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Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach.

2021 JMIR medical informatics
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Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City.

2020 BMJ Open
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Federated Learning of Electronic Health Records Improves Mortality Prediction in Patients Hospitalized with COVID-19

2020 medRxiv
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