Connection

Co-Authors

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This is a "connection" page, showing publications co-authored by Akhil Vaid and Riccardo Miotto.
Akhil Vaid
Connection Strength
0.631
Riccardo Miotto
  1. Federated Learning of Electronic Health Records to Improve Mortality Prediction in Hospitalized Patients With COVID-19: Machine Learning Approach. JMIR Med Inform. 2021 Jan 27; 9(1):e24207.
    View in: PubMed
    Score: 0.232
  2. 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 11 06; 22(11):e24018.
    View in: PubMed
    Score: 0.228
  3. Relational Learning Improves Prediction of Mortality in COVID-19 in the Intensive Care Unit. IEEE Trans Big Data. 2021 Mar; 7(1):38-44.
    View in: PubMed
    Score: 0.058
  4. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City. BMJ Open. 2020 11 27; 10(11):e040736.
    View in: PubMed
    Score: 0.057
  5. AKI in Hospitalized Patients with COVID-19. J Am Soc Nephrol. 2021 01; 32(1):151-160.
    View in: PubMed
    Score: 0.056
Connection Strength

The connection strength for concepts is the sum of the scores for each matching publication.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.