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Charlotta Lindvall, MD, PhD


Palliative Medicine

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Physician

  • Physician
  • Instructor in Medicine, Harvard Medical School

Centers/Programs

Contact Information

  • Office Phone Number617-632-6464

Board Certification:

  • Hospice and Palliative Medicine
  • Internal Medicine

Fellowship:

  • Dana-Farber Cancer Institute and Massachusetts General Hospital Palliative Care

Residency:

  • MSU College of Human Medicine

Medical School:

  • Karolinska Institutet, Stockholm, Sweden

Research

Natural Language Processing to Identify Advance Care Planning Documentation in a Multisite Pragmatic Clinical Trial. J Pain Symptom Manage. 2021 Jul 13.
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Management of Pneumoperitoneum: Role and Limits of Nonoperative Treatment. Ann Surg. 2021 Jul 01; 274(1):146-154.
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Deep learning to predict long-term mortality in patients requiring 7 days of mechanical ventilation. PLoS One. 2021; 16(6):e0253443.
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Using nursing notes to improve clinical outcome prediction in intensive care patients: A retrospective cohort study. J Am Med Inform Assoc. 2021 Apr 21.
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Mining heterogeneous clinical notes by multi-modal latent topic model. PLoS One. 2021; 16(4):e0249622.
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A Yet Unrealized Promise: Structured Advance Care Planning Elements in the Electronic Health Record. J Palliat Med. 2021 Apr 07.
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Artificial Intelligence-Aided Precision Medicine for COVID-19: Strategic Areas of Research and Development. J Med Internet Res. 2021 03 12; 23(3):e22453.
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Natural Language Processing to Assess Frequency of Functional Status Documentation for Patients Newly Diagnosed With Colorectal Cancer-Reply. JAMA Oncol. 2021 Mar 01; 7(3):463.
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Expert Stakeholder Prioritization of Process Quality Measures to Achieve Patient- and Family-Centered Palliative and End-of-Life Cancer Care. J Palliat Med. 2021 Feb 19.
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Exploring the Process of Cancer Care for Patients With Pre-Existing Mobility Disability. JCO Oncol Pract. 2021 01; 17(1):e53-e61.
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Prevalence, Management and Outcomes Related to Preoperative Medical Orders for life Sustaining Treatment (MOLST) in an Adult Surgical Population. Ann Surg. 2020 Dec 18; Publish Ahead of Print.
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Latent bias and the implementation of artificial intelligence in medicine. J Am Med Inform Assoc. 2020 12 09; 27(12):2020-2023.
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Use of Natural Language Processing to Assess Frequency of Functional Status Documentation for Patients Newly Diagnosed With Colorectal Cancer. JAMA Oncol. 2020 10 01; 6(10):1628-1630.
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Identifying Goals of Care Conversations in the Electronic Health Record Using Natural Language Processing and Machine Learning. J Pain Symptom Manage. 2021 01; 61(1):136-142.e2.
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Measure Scan and Synthesis of Palliative and End-of-Life Process Quality Measures for Advanced Cancer. JCO Oncol Pract. 2021 02; 17(2):e140-e148.
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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.
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Shortlist of Quality Indicators for End-of-Life Cancer Care. JAMA Oncol. 2020 07 01; 6(7):1118-1119.
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Adherence and Concordance between Serious Illness Care Planning Conversations and Oncology Clinician Documentation among Patients with Advanced Cancer. J Palliat Med. 2021 01; 24(1):53-62.
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Deep Natural Language Processing to Identify Symptom Documentation in Clinical Notes for Patients With Heart Failure Undergoing Cardiac Resynchronization Therapy. J Pain Symptom Manage. 2020 11; 60(5):948-958.e3.
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Long-Term Prognosis of Older Adults Who Survive Emergency Mechanical Ventilation. J Pain Symptom Manage. 2020 11; 60(5):1019-1026.
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Challenges of Developing a Natural Language Processing Method With Electronic Health Records to Identify Persons With Chronic Mobility Disability. Arch Phys Med Rehabil. 2020 10; 101(10):1739-1746.
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Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artif Intell Med. 2020 04; 104:101822.
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Deep Natural Language Processing Identifies Variation in Care Preference Documentation. J Pain Symptom Manage. 2020 06; 59(6):1186-1194.e3.
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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.
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Can machine learning improve patient selection for cardiac resynchronization therapy? PLoS One. 2019; 14(10):e0222397.
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Variation in Serious Illness Communication among Surgical Patients Receiving Palliative Care. J Palliat Med. 2020 03; 23(3):411-414.
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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.
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Natural Language Processing Accurately Measures Adherence to Best Practice Guidelines for Palliative Care in Trauma. J Pain Symptom Manage. 2020 02; 59(2):225-232.e2.
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Deficits in the Palliative Care Process Measures in Patients with Advanced Pancreatic Cancer Undergoing Operative and Invasive Nonoperative Palliative Procedures. Ann Surg Oncol. 2019 Dec; 26(13):4204-4212.
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Documentation of Palliative and End-of-Life Care Process Measures Among Young Adults Who Died of Cancer: A Natural Language Processing Approach. J Adolesc Young Adult Oncol. 2020 02; 9(1):100-104.
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US National Trends in Opioid-Related Hospitalizations Among Patients With Cancer. JAMA Oncol. 2019 May 01; 5(5):734-735.
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Risk factors and prevalence of liver disease in review of 2557 routine liver biopsies performed during bariatric surgery. Surg Obes Relat Dis. 2019 Jun; 15(6):843-849.
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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.
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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.
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Deep learning algorithms to identify documentation of serious illness conversations during intensive care unit admissions. Palliat Med. 2019 02; 33(2):187-196.
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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.
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Needle in a Haystack: Natural Language Processing to Identify Serious Illness. J Palliat Med. 2019 02; 22(2):179-182.
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Machine Learning to Predict, Detect, and Intervene Older Adults Vulnerable for Adverse Drug Events in the Emergency Department. J Med Toxicol. 2018 09; 14(3):248-252.
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Postoperative outcomes in patients with a do-not-resuscitate (DNR) order undergoing elective procedures. J Clin Anesth. 2018 Aug; 48:81-88.
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Measuring Processes of Care in Palliative Surgery: A Novel Approach Using Natural Language Processing. Ann Surg. 2018 05; 267(5):823-825.
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Machine Learning Methods to Extract Documentation of Breast Cancer Symptoms From Electronic Health Records. J Pain Symptom Manage. 2018 06; 55(6):1492-1499.
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A Pilot Study to Evaluate Compliance with Guidelines for Preprocedural Reconsideration of Code Status Limitations. J Palliat Med. 2018 08; 21(8):1152-1156.
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Using Electronic Health Records for Quality Measurement and Accountability in Care of the Seriously Ill: Opportunities and Challenges. J Palliat Med. 2018 03; 21(S2):S52-S60.
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Prognosis, Communication, and Advance Care Planning in Heart Failure: A Module for Students, Residents, Fellows, and Practicing Clinicians. MedEdPORTAL. 2017 Jun 16; 13:10596.
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Increasing sex differences in the use of cardiac resynchronization therapy with or without implantable cardioverter-defibrillator. Eur Heart J. 2017 May 14; 38(19):1485-1494.
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The Intensive Palliative Care Unit: Changing Outcomes for Hospitalized Cancer Patients in an Academic Medical Center. J Palliat Med. 2017 03; 20(3):285-289.
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National Trends in the Use of Cardiac Resynchronization Therapy With or Without Implantable Cardioverter-Defibrillator. Circulation. 2016 Jan 19; 133(3):273-81.
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The Intensive Palliative Care Unit (IPCU): Changing outcomes for hospitalized cancer patients in an academic medical center. J Clin Oncol. 2015 Oct 10; 33(29_suppl):135.
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Teaching colleagues how to discuss prognosis as part of a hospital-wide quality improvement project: the positive impact of a 90-minute workshop. J Pain Symptom Manage. 2015 May; 49(5):960-3.
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Overcoming the barriers to palliative care referral for patients with advanced heart failure. J Am Heart Assoc. 2014 Feb 28; 3(1):e000742.
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Telomerase reverse transcriptase promotes epithelial-mesenchymal transition and stem cell-like traits in cancer cells. Oncogene. 2013 Sep 05; 32(36):4203-13.
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LRP6 mediates cAMP generation by G protein-coupled receptors through regulating the membrane targeting of Ga(s). Sci Signal. 2011 Mar 15; 4(164):ra15.
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Mammalian target of rapamycin-dependent acinar cell neoplasia after inactivation of Apc and Pten in the mouse salivary gland: implications for human acinic cell carcinoma. Cancer Res. 2010 Nov 15; 70(22):9143-52.
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The Wnt receptor, Lrp5, is expressed by mouse mammary stem cells and is required to maintain the basal lineage. PLoS One. 2009 Aug 12; 4(8):e6594.
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The Wnt co-receptor Lrp6 is required for normal mouse mammary gland development. PLoS One. 2009 Jun 05; 4(6):e5813.
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Gene targeting approaches in mice: assessing the roles of LRP5 and LRP6 in osteoblasts. J Musculoskelet Neuronal Interact. 2008 Oct-Dec; 8(4):291-3.
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Cdc7-Dbf4 kinase overexpression in multiple cancers and tumor cell lines is correlated with p53 inactivation. Neoplasia. 2008 Sep; 10(9):920-31.
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Wnt signaling, stem cells, and the cellular origin of breast cancer. Stem Cell Rev. 2007 Jun; 3(2):157-68.
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Phosphoinositide 3-kinase regulates a subset of interferon-alpha-stimulated genes. Exp Cell Res. 2007 Jan 15; 313(2):404-14.
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The Wnt signaling receptor Lrp5 is required for mammary ductal stem cell activity and Wnt1-induced tumorigenesis. J Biol Chem. 2006 Nov 17; 281(46):35081-7.
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TSC2 integrates Wnt and energy signals via a coordinated phosphorylation by AMPK and GSK3 to regulate cell growth. Cell. 2006 Sep 08; 126(5):955-68.
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Epstein-Barr virus latent membrane 2A (LMP2A) down-regulates telomerase reverse transcriptase (hTERT) in epithelial cell lines. Int J Cancer. 2005 Jan 10; 113(2):284-9.
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Combined genetic and transcriptional profiling of acute myeloid leukemia with normal and complex karyotypes. Haematologica. 2004 Sep; 89(9):1072-81.
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Expression analyses identify MLL as a prominent target of 11q23 amplification and support an etiologic role for MLL gain of function in myeloid malignancies. Blood. 2004 Jan 01; 103(1):229-35.
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Molecular characterization of human telomerase reverse transcriptase-immortalized human fibroblasts by gene expression profiling: activation of the epiregulin gene. Cancer Res. 2003 Apr 15; 63(8):1743-7.
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Deletion of the telomerase reverse transcriptase gene and haploinsufficiency of telomere maintenance in Cri du chat syndrome. Am J Hum Genet. 2003 Apr; 72(4):940-8.
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Genomic characterization of MOZ/CBP and CBP/MOZ chimeras in acute myeloid leukemia suggests the involvement of a damage-repair mechanism in the origin of the t(8;16)(p11;p13). Genes Chromosomes Cancer. 2003 Jan; 36(1):90-8.
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Amplification of the telomerase reverse transcriptase (hTERT) gene in cervical carcinomas. Genes Chromosomes Cancer. 2002 Jul; 34(3):269-75.
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Simian AIDS-related lymphoma growth in severe combined immunodeficiency mice is independent of karyotypic abnormalities or Bcl-6 mutations. AIDS Res Hum Retroviruses. 2002 Mar 20; 18(5):383-90.
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Molecular cytogenetic characterization of acute myeloid leukemia and myelodysplastic syndromes with multiple chromosome rearrangements. Haematologica. 2001 Nov; 86(11):1158-64.
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Frequent amplification of the telomerase reverse transcriptase gene in human tumors. Cancer Res. 2000 Nov 15; 60(22):6230-5.
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RT-PCR analysis of the MOZ-CBP and CBP-MOZ chimeric transcripts in acute myeloid leukemias with t(8;16)(p11;p13). Genes Chromosomes Cancer. 2000 Aug; 28(4):415-24.
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