Harvard Catalyst Profiles

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Kenneth David Mandl, M.D.

Co-Author

This page shows the publications co-authored by Kenneth Mandl and Alon Geva.
Connection Strength

4.172
  1. A high-throughput phenotyping algorithm is portable from adult to pediatric populations. J Am Med Inform Assoc. 2021 06 12; 28(6):1265-1269.
    View in: PubMed
    Score: 0.942
  2. Adverse drug event presentation and tracking (ADEPT): semiautomated, high throughput pharmacovigilance using real-world data. JAMIA Open. 2020 Oct; 3(3):413-421.
    View in: PubMed
    Score: 0.892
  3. Adverse drug event rates in pediatric pulmonary hypertension: a comparison of real-world data sources. J Am Med Inform Assoc. 2020 02 01; 27(2):294-300.
    View in: PubMed
    Score: 0.857
  4. Provider Connectedness to Other Providers Reduces Risk of Readmission After Hospitalization for Heart Failure. Med Care Res Rev. 2019 02; 76(1):115-128.
    View in: PubMed
    Score: 0.717
  5. Data-driven clustering identifies features distinguishing multisystem inflammatory syndrome from acute COVID-19 in children and adolescents. EClinicalMedicine. 2021 Oct; 40:101112.
    View in: PubMed
    Score: 0.239
  6. A Computable Phenotype Improves Cohort Ascertainment in a Pediatric Pulmonary Hypertension Registry. J Pediatr. 2017 09; 188:224-231.e5.
    View in: PubMed
    Score: 0.179
  7. Learning a Comorbidity-Driven Taxonomy of Pediatric Pulmonary Hypertension. Circ Res. 2017 Aug 04; 121(4):341-353.
    View in: PubMed
    Score: 0.178
  8. Validation of an internationally derived patient severity phenotype to support COVID-19 analytics from electronic health record data. J Am Med Inform Assoc. 2021 07 14; 28(7):1411-1420.
    View in: PubMed
    Score: 0.059
  9. International Analysis of Electronic Health Records of Children and Youth Hospitalized With COVID-19 Infection in 6 Countries. JAMA Netw Open. 2021 06 01; 4(6):e2112596.
    View in: PubMed
    Score: 0.059
  10. Feature extraction for phenotyping from semantic and knowledge resources. J Biomed Inform. 2019 03; 91:103122.
    View in: PubMed
    Score: 0.050
Connection Strength
The connection strength for co-authors is the sum of the scores for each of their shared publications.

Publication scores are based on many factors, including how long ago they were written and whether the person is a first or senior author.
Funded by the NIH National Center for Advancing Translational Sciences through its Clinical and Translational Science Awards Program, grant number UL1TR002541.