A year of forming collaborations

June 1, 2021, marks my 1-year anniversary with the UAB Department of Anesthesiology and Perioperative Medicine. This last year has been one filled with new partnerships and exciting data science projects. Working here, I have formed connections across the department, hospital, campus, and other institutions (visualized in the figure below).

Network visualization of collaborations within the department (light blue), hospital and campus (green), and other institutions (gray).

Within the department, my research collaborations have focused on the development of artificial intelligence (AI) systems to reduce the cognitive load on clinicians. The bulk of these projects have been through joining research projects initiated by clinical faculty members. In these projects, Perioperative Data Science has developed AI methods that address non-hypothesis-driven questions. Some examples are

  • Predicting acute and delay kidney injuries from perioperative factors, past medical history, and biomarkers, [1]
  • Predicting blood transfusion product needs in high-risk cardiac surgery, [2-4]
  • Determining patient-specific blood pressure requirements during cardiac surgery,
  • Predicting post-PACU (post-anesthesia care unit) escalations of care,
  • Predicting outcomes of low intraoperative mean arterial pressure, and
  • Predicting bleeding risk for patients receiving heparin.

Across, UAB, I have collaborated with bioinformatics faculty, post-docs, and graduate students to develop a COVID-19 risk scorecard [5] and submit an application for an NIH grant to support building an AI to support patient nutrition initiatives. Collaborators from the UAB Department of Radiology and I recently submitted two abstracts and wrapped up a manuscript on materials for training clinicians in the appropriate use of AI tools [6-7].

With collaborators at UAB and Wake Forest, I am working on stratifying patients by risk for opioid-induced respiratory depression for enhanced monitoring. I have also initiated a multi-institution project to develop a zero-code machine learning software package that will both speed AI projects to machine learning experts and enable machine learning research for non-experts. I have also worked closely with the Sickbay(TM) from Medical Informatics Corp. development team to make sure our researchers’ needs are met by the tools Sickbay provides and assist in creating new tools when they are not. Additionally, I have initiated discussions with industry partners for sponsored research related to our clinical faculty’s work.

It has been an invigorating year of forming many connections. I look forward to even more in year 2!

[1] A. Zaky et al. (2021), “End-of-Procedure Volume Responsiveness Defined by the Passive Leg Raise Test Is Not Associated With Acute Kidney Injury After Cardiopulmonary Bypass,” J. Cardiothorac. Vasc. Anesth., vol. 35, no. 5, pp. 1299–1306, 2021, doi: 10.1053/j.jvca.2020.11.022.

[2] R.L. Melvin (presenter), D. Mladinov, L. Padilla, D.E. Berkowitz “Comparison of Supervised Machine Learning Techniques for Prediction of Blood Products Transfusion after High-Risk Cardiac Surgery,” at Society of Critical Care Anesthesiologists 2021 Annual Meeting, Virtual.

[3] R.L. Melvin (presenter), D. Mladinov, L. Padilla, D.E. Berkowitz “Comparison of Supervised Machine Learning Techniques for Prediction of Blood Products Transfusion after High-Risk Cardiac Surgery,” at International Anesthesia Research Society 2021 Annual Meeting. Virtual.

[4] R.L. Melvin (presenter), D. Mladinov, L. Padilla, D.E. Berkowitz “Comparison of Supervised Machine Learning Techniques for Prediction of Blood Products Transfusion after High-Risk Cardiac Surgery,” at Association of University Anesthesiologists 2021 Annual Meeting. Virtual.

[5] T.K. Kumar Mamidi, T.K. Tran-Nguyen, R.L. Melvin, E.A. Worthey (2021) “Development of An Individualized Risk Prediction Model for COVID-19 Using Electronic Health Record Data.” Front. Big Data 4:675882. doi: 10.3389/fdata.2021.675882

[6] A.M.A. Elkassem, A.M.A. (presenter), D. Nachand, J.D. Perchik, R. Mresh, M. Anderson, R.L. Melvin, A.D., Smith (2021) “Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of AI Algorithms in Abdominal Radiology,” submitted to SABI 2021. Washington, D.C.

[7] A.M.A. Elkassem, A.M.A. (presenter), D. Nachand, J.D. Perchik, R. Mresh, M. Anderson, R.L. Melvin, A.D., Smith (2021) “Strengths, Weaknesses, Opportunities, and Threats (SWOT) Analysis of AI Algorithms in Abdominal Radiology,” submitted to RSNA 2021. Chicago, IL.

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