Harvard Catalyst Profiles

Contact, publication, and social network information about Harvard faculty and fellows.

HMS faculty, help us improve the algorithms in Profiles by uploading your CV!

Charlotta Lindvall, Ph.D., M.D.

Co-Author

This page shows the publications co-authored by Charlotta Lindvall and James Tulsky.
Connection Strength

4.446
  1. In-hospital mortality in older patients after ventricular assist device implantation: A national cohort study. J Thorac Cardiovasc Surg. 2019 08; 158(2):466-475.e4.
    View in: PubMed
    Score: 0.764
  2. Natural Language Processing to Assess End-of-Life Quality Indicators in Cancer Patients Receiving Palliative Surgery. J Palliat Med. 2019 02; 22(2):183-187.
    View in: PubMed
    Score: 0.760
  3. Associations Between Family Member Involvement and Outcomes of Patients Admitted to the Intensive Care Unit: Retrospective Cohort Study. JMIR Med Inform. 2022 Jun 15; 10(6):e33921.
    View in: PubMed
    Score: 0.245
  4. Natural Language Processing for Computer-Assisted Chart Review to Assess Documentation of Substance use and Psychopathology in Heart Failure Patients Awaiting Cardiac Resynchronization Therapy. J Pain Symptom Manage. 2022 Oct; 64(4):400-409.
    View in: PubMed
    Score: 0.245
  5. Deep Learning for Cancer Symptoms Monitoring on the Basis of Electronic Health Record Unstructured Clinical Notes. JCO Clin Cancer Inform. 2022 06; 6:e2100136.
    View in: PubMed
    Score: 0.244
  6. Association of an Advance Care Planning Video and Communication Intervention With Documentation of Advance Care Planning Among Older Adults: A Nonrandomized Controlled Trial. JAMA Netw Open. 2022 02 01; 5(2):e220354.
    View in: PubMed
    Score: 0.239
  7. Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study. J Am Med Inform Assoc. 2021 07 30; 28(8):1660-1666.
    View in: PubMed
    Score: 0.230
  8. Natural Language Processing to Assess Palliative Care and End-of-Life Process Measures in Patients With Breast Cancer With Leptomeningeal Disease. Am J Hosp Palliat Care. 2020 May; 37(5):371-376.
    View in: PubMed
    Score: 0.204
  9. Can machine learning improve patient selection for cardiac resynchronization therapy? PLoS One. 2019; 14(10):e0222397.
    View in: PubMed
    Score: 0.203
  10. Differences by Race, Religiosity, and Mental Health in Preferences for Life-Prolonging Treatment Among Medicare Beneficiaries. J Gen Intern Med. 2019 10; 34(10):1981-1983.
    View in: PubMed
    Score: 0.203
  11. US National Trends in Opioid-Related Hospitalizations Among Patients With Cancer. JAMA Oncol. 2019 May 01; 5(5):734-735.
    View in: PubMed
    Score: 0.197
  12. Goals-of-Care Conversations for Older Adults With Serious Illness in the Emergency Department: Challenges and Opportunities. Ann Emerg Med. 2019 08; 74(2):276-284.
    View in: PubMed
    Score: 0.194
  13. Deep learning algorithms to identify documentation of serious illness conversations during intensive care unit admissions. Palliat Med. 2019 02; 33(2):187-196.
    View in: PubMed
    Score: 0.191
  14. Needle in a Haystack: Natural Language Processing to Identify Serious Illness. J Palliat Med. 2019 02; 22(2):179-182.
    View in: PubMed
    Score: 0.189
  15. Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records. J Pain Symptom Manage. 2018 06; 55(6):1492-1499.
    View in: PubMed
    Score: 0.182
  16. A Yet Unrealized Promise: Structured Advance Care Planning Elements in the Electronic Health Record. J Palliat Med. 2021 08; 24(8):1221-1225.
    View in: PubMed
    Score: 0.056
  17. Advance Care Planning: Promoting Effective and Aligned Communication in the Elderly (ACP-PEACE): the study protocol for a pragmatic stepped-wedge trial of older patients with cancer. BMJ Open. 2020 07 14; 10(7):e040999.
    View in: PubMed
    Score: 0.054
  18. Measuring Processes of Care in Palliative Surgery: A Novel Approach Using Natural Language Processing. Ann Surg. 2018 05; 267(5):823-825.
    View in: PubMed
    Score: 0.046
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.