{"id":2269,"date":"2021-09-03T13:53:22","date_gmt":"2021-09-03T17:53:22","guid":{"rendered":"https:\/\/labs.icahn.mssm.edu\/minervalab\/?page_id=2269"},"modified":"2025-03-27T17:51:00","modified_gmt":"2025-03-27T21:51:00","slug":"scientific-computing-publications","status":"publish","type":"page","link":"https:\/\/labs.icahn.mssm.edu\/minervalab\/publications\/scientific-computing-publications\/","title":{"rendered":"Scientific Computing Publications"},"content":{"rendered":"

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Scientific Computing and Data<\/a>\u00a0\/\u00a0High Performance Computing<\/a>\u00a0\/ Publications<\/a> \/ Kovatch Publications<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”3.25″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”][et_pb_column type=”4_4″ _builder_version=”3.25″ custom_padding=”|||” custom_padding__hover=”|||”][et_pb_text admin_label=”Patricia Publications List” _builder_version=”4.9.0″ header_font=”|700|||||||” header_text_color=”#221f72″ background_size=”initial” background_position=”top_left” background_repeat=”repeat”]<\/p>\n

Publications Authored & Co-Authored by Patricia Kovatch, Dean of Scientific Computing and Data<\/h3>\n
    \n
  1. Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension<\/a>. (Dec 2024) F Gulamali, P Jayaraman, P Kovatch, GN Nadkarni, et. al.<\/li>\n
  2. Measurement of circulating viral antigens post-SARS-CoV-2 infection in a multicohort study<\/a>. (Dec 2024) Z Swank, E Borberg, P Kovatch, TF Bumol, et. al.<\/li>\n
  3. Multimodal fusion learning for long QT syndrome pathogenic genotypes in a racially diverse population<\/a>. (Dec 2024) J Jiang, HM Thi Vy, P Kovatch, GN Nadkarni, et. al.<\/li>\n
  4. Local large language models for privacy-preserving accelerated review of historic echocardiogram reports<\/a>. (Sept 2024) A Vaid, SQ Duong, P Kovatch, GN Nadkarni, et. al.<\/li>\n
  5. A novel method leveraging time series data to improve subphenotyping and application in critically ill patients with COVID-19.<\/a> (Feb 2024) by W Oh, P Jayaraman, P Kovatch, and GN Nadkarni, et. al.<\/li>\n
  6. Ninth Computational Approaches for Cancer Workshop (CAFCW23)<\/a> (Nov 2023) by E Stahlberg, P Kovatch, L Borkon, et. al.<\/li>\n
  7. Implications of the Use of Artificial Intelligence Predictive Models in Health Care Settings<\/a>. (Oct 2023) by A Vaid, A Sawant, P Kovatch, GN Nadkarni, et. al.<\/li>\n
  8. Returning integrated genomic risk and clinical recommendations: The eMERGE study<\/a>. (Apr 2023) by eMERGE Consortium, P Kovatch, et. al.<\/li>\n
  9. Autoencoders for sample size estimation for fully connected neural network classifiers. <\/a>\u00a0(Dec 2022) by F Gulamali, AS Sawant, P Kovatch, A Charney, GN Nadkarni, et. al.<\/li>\n
  10. Quantitative chest computed tomography combined with plasma cytokines predict outcomes in COVID-19 patients<\/a> (August 2022) by P. Kovatch, S. Nirenberg, M. Merad, S. Gnjatic, et al.<\/li>\n
  11. Automated Determination of Left Ventricular Function Using Electrocardiogram Data in Patients on Maintenance Hemodialysis<\/a> (July 2022) by P. Kovatch, A. Charney, G. Nadkarni, et al.<\/li>\n
  12. Multi-ethnic Investigation of Risk and Immune Determinants of COVID-19 Outcomes<\/a> (March 2022) by P. Kovatch, S. Nirenberg, et al.<\/li>\n
  13. Functional Effects of Cardiomyocyte Injury in COVID-19<\/a>. (Jan 2022) by M Siddiq, AT Chan, P Kovatch, R Iyengar., et. al.<\/li>\n
  14. Using sequence clustering to identify clinically relevant subphenotypes in patients with COVID-19 admitted to the intensive care unit<\/a> (Jan 2022) by P. Kovatch, A. Charney, G. Nadkarni, et al.<\/li>\n
  15. Quantitative chest CT combined with plasma cytokines predict outcomes in COVID-19 patients<\/a> (Oct 2021) by P. Kovatch, et al.<\/li>\n
  16. Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19<\/a> (Aug 2021) by P. Kovatch, A. Vaid et al.<\/li>\n
  17. Prediction of individual COVID-19 diagnosis using baseline demographics and lab data<\/a> (July 2021) by P. Kovatch, J. Zhang et al.<\/li>\n
  18. Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City<\/a> (July 2021) by P. Kovatch, S. K. Jaladanki et al.<\/li>\n
  19. Analysis of sex-specific risk factors and clinical outcomes in COVID-19<\/a> (June 2021) by P. Kovatch, S. Nirenberg, et al.<\/li>\n
  20. Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19<\/a> (March 2021) by P. Kovatch, L. Chan et al.<\/li>\n
  21. GU\u00cdA: a digital platform to facilitate result disclosure in genetic counseling<\/a> (Feb 2021) by P. Kovatch, S. A. Suckiel et al.<\/li>\n
  22. The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of New York City children<\/a> (Jan 2021) by P. Kovatch, J. A. Odgis et al.<\/li>\n
  23. Physiology of cardiomyocyte injury in COVID-19<\/a> (Nov 2020) by P. Kovatch, M. M. Siddiq et al.<\/li>\n
  24. Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients<\/a> (Nov 2020) by P. Kovatch, A. Vaid et al.<\/li>\n
  25. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City<\/a> (Nov 2020) by P. Kovatch, I. Paranjpe et al.<\/li>\n
  26. An inflammatory cytokine signature predicts COVID-19 severity and survival<\/a> (Oct 2020) by P. Kovatch, D. M. Del Valle et al.<\/li>\n
  27. Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records<\/a> (Oct 2020) by P. Kovatch, Z. Wang et al.<\/li>\n
  28. COVID, Cancer, and HPC Highlight the Computational Approaches for Cancer Workshop at SC<\/a> (Aug 2020) through SC20<\/li>\n
  29. Optimizing High-Performance Computing Systems for Biomedical Workloads<\/a> (July 2020) by P. Kovatch et al.<\/li>\n
  30. An inflammatory cytokine signature helps predict COVID-19 severity and death<\/a> (May 2020) by P. Kovatch, D. M. Del Valle et al.<\/li>\n
  31. Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining<\/a> (Sept 2018), P. Kovatch, K. Shameer et al.<\/li>\n
  32. Big and disparate data: considerations for pediatric consortia<\/a> (April 2017) by P. Kovatch, J. A. Stingone et al.<\/li>\n
  33. Preserving Data: Who\u2019s Got Your Back(up)?<\/a> (Feb 2016) through ACSB: An International Forum for Cell Biology<\/li>\n
  34. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai heart failure cohort<\/a> (2016) by P. Kovatch, K. Shameer et al.<\/li>\n
  35. Pharmacological factors associated with congestive heart failure hospital readmission: a case-study using 15,768 heart failure patients from two health systems<\/a> (2016) by P. Kovatch, K. Johnson et al.<\/li>\n
  36. Accelerating child health exposure research with the CHEAR data center (Oct 2016) by P. Kovatch, D.L. McGuinness et al.<\/li>\n
  37. Lessons learned from the Children\u2019s Health Exposure Analysis Resource (CHEAR) data center (Aug 2016) by P. Kovatch, D.L. McGuinness et al.<\/li>\n
  38. Big Omics Data Experience<\/a> (Nov 2015) by P. Kovatch et al.<\/li>\n
  39. Improved grid security posture through multi-factor authentication<\/a> (Sept 2011) by P. Kovatch, V. Hazelwood et al.<\/li>\n
  40. The Malthusian catastrophe is upon us! Are the largest HPC machines ever up?<\/a> (Aug 2011) by P. Kovatch et al.<\/li>\n
  41. Increasing petascale resource utilization through bimodal scheduling policies<\/a> (May 2011) by P. Kovatch, P. Andrews et al.<\/li>\n
  42. Scheduling diverse high performance computing systems with the goal of maximizing utilization<\/a> (Feb 2011) by P. Kovatch, T. Samuel et al.<\/li>\n<\/ol>\n

     <\/p>\n

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    Scientific Computing and Data\u00a0\/\u00a0High Performance Computing\u00a0\/ Publications \/ Kovatch PublicationsPublications Authored & Co-Authored by Patricia Kovatch, Dean of Scientific Computing and Data Derivation, external and clinical validation of a deep learning approach for detecting intracranial hypertension. (Dec 2024) F Gulamali, P Jayaraman, P Kovatch, GN Nadkarni, et. al. Measurement of circulating viral antigens post-SARS-CoV-2 infection […]<\/p>\n","protected":false},"author":600,"featured_media":0,"parent":2288,"menu_order":0,"comment_status":"closed","ping_status":"closed","template":"","meta":{"_et_pb_use_builder":"on","_et_pb_old_content":"

    Authored & Co-Authored by Patricia Kovatch, Dean of Scientific Computing and Data<\/strong><\/p>

    1. Predictive Approaches for Acute Dialysis Requirement and Death in COVID-19<\/a> (Aug 2021) by P. Kovatch, A. Vaid et al.<\/li>
    2. Prediction of individual COVID-19 diagnosis using baseline demographics and lab data<\/a> (July 2021) by P. Kovatch, J. Zhang et al.<\/li>
    3. Development of a federated learning approach to predict acute kidney injury in adult hospitalized patients with COVID-19 in New York City<\/a> (July 2021) by P. Kovatch, S. K. Jaladanki et al.<\/li>
    4. Outcomes of Patients on Maintenance Dialysis Hospitalized with COVID-19<\/a> (March 2021) by P. Kovatch, L. Chan et al.<\/li>
    5. GU\u00cdA: a digital platform to facilitate result disclosure in genetic counseling<\/a> (Feb 2021) by P. Kovatch, S. A. Suckiel et al.<\/li>
    6. The NYCKidSeq project: study protocol for a randomized controlled trial incorporating genomics into the clinical care of diverse New York City children<\/a> (Jan 2021) by P. Kovatch, J. A. Odgis et al.<\/li>
    7. Physiology of cardiomyocyte injury in COVID-19<\/a> (Nov 2020) by P. Kovatch, M. M. Siddiq et al.<\/li>
    8. Machine Learning to Predict Mortality and Critical Events in COVID-19 Positive New York City Patients<\/a> (Nov 2020) by P. Kovatch, A. Vaid et al.<\/li>
    9. Retrospective cohort study of clinical characteristics of 2199 hospitalised patients with COVID-19 in New York City<\/a> (Nov 2020) by P. Kovatch, I. Paranjpe et al.<\/li>
    10. An inflammatory cytokine signature predicts COVID-19 severity and survival<\/a> (Oct 2020) by P. Kovatch, D. M. Del Valle et al.<\/li>
    11. Hospitalised COVID-19 patients of the Mount Sinai Health System: a retrospective observational study using the electronic medical records<\/a> (Oct 2020) by P. Kovatch, Z. Wang et al.<\/li>
    12. COVID, Cancer, and HPC Highlight the Computational Approaches for Cancer Workshop at SC<\/a> (Aug 2020) through SC20<\/li>
    13. Optimizing High-Performance Computing Systems for Biomedical Workloads<\/a> (July 2020) by P. Kovatch et al.<\/li>
    14. An inflammatory cytokine signature helps predict COVID-19 severity and death<\/a> (May 2020) by P. Kovatch, D. M. Del Valle et al.<\/li>
    15. Optimizing High-Performance Computing Systems for Biomedical Workloads<\/a> (May 2020) by P. Kovatch<\/li>
    16. Pharmacological risk factors associated with hospital readmission rates in a psychiatric cohort identified using prescriptome data mining<\/a> (Sept 2018), P. Kovatch, K. Shameer et al.<\/li>
    17. Big and disparate data: considerations for pediatric consortia<\/a> (April 2017) by P. Kovatch, J. A. Stingone et al.<\/li>
    18. Preserving Data: Who\u2019s Got Your Back(up)?<\/a> (Feb 2016) through ACSB: An International Forum for Cell Biology<\/li>
    19. Predictive modeling of hospital readmission rates using electronic medical record-wide machine learning: a case-study using Mount Sinai heart failure cohort<\/a> (2016) by P. Kovatch, K. Shameer et al.<\/li>
    20. Pharmacological factors associated with congestive heart failure hospital readmission: a case-study using 15,768 heart failure patients from two health systems<\/a> (2016) by P. Kovatch, K. Johnson et al.<\/li>
    21. Accelerating child health exposure research with the CHEAR data center (Oct 2016) by P. Kovatch, D.L. McGuinness et al.<\/li>
    22. Lessons learned from the Children\u2019s Health Exposure Analysis Resource (CHEAR) data center (Aug 2016) by P. Kovatch, D.L. McGuinness et al.<\/li>
    23. Big Omics Data Experience<\/a> (Nov 2015) by P. Kovatch et al.<\/li>
    24. Improved grid security posture through multi-factor authentication<\/a> (Sept 2011) by P. Kovatch, V. Hazelwood et al.<\/li>
    25. The Malthusian catastrophe is upon us! Are the largest HPC machines ever up?<\/a> (Aug 2011) by P. Kovatch et al.<\/li>
    26. Increasing petascale resource utilization through bimodal scheduling policies<\/a> (May 2011) by P. Kovatch, P. Andrews et al.<\/li>
    27. Scheduling diverse high performance computing systems with the goal of maximizing utilization<\/a> (Feb 2011) by P. Kovatch, T. Samuel et al.<\/li><\/ol>

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