Andrew Bishara, MD
Assistant Professor
Anesthesia
School of Medicine

My main research aim is to better predict and prevent complications in surgical patients using advanced machine learning techniques. My work includes: Real-Time Risk Assessment: Creating models to predict acute kidney injury (AKI), pain, delirium, and blood loss during surgeries in real-time. Model Validation: Ensuring these models are reliable with the eventual goal of pursuing regulatory approval. I strive to build models that I would want to use when caring for patients. Additionally, my research explores the space of computer-human interfaces related to artificial intelligence (AI) to seamlessly integrate these models into clinical workflows.

Clinical Practice As an anesthesiologist, I am committed to providing safe and effective patient care in the operating room. My clinical work is closely linked with my research, fostering an environment where new insights directly benefit patient outcomes.

Commitment to Progress I value the opportunity to contribute to meaningful advancements in healthcare. By integrating innovative technology into clinical practices, I seek to improve patient outcomes and advance the field of perioperative medicine.

• Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.
  NPJ digital medicine 2022 Feng J, Phillips RV, Malenica I, Bishara A, Hubbard AE, Celi LA, Pirracchio R
• Opioid prescribing practices at hospital discharge for surgical patients before and after the Centers for Disease Control and Prevention's 2016 opioid prescribing guideline.
  BMC anesthesiology 2022 Langnas E, Bishara A, Croci R, Rodriguez-Monguio R, Wick EC, Chen CL, Guan Z
• Considerations for the implementation of machine learning into acute care settings.
  British medical bulletin 2022 Bishara A, Maze EH, Maze M
• Postoperative delirium prediction using machine learning models and preoperative electronic health record data.
  BMC anesthesiology 2022 Bishara A, Chiu C, Whitlock EL, Douglas VC, Lee S, Butte AJ, Leung JM, Donovan AL
• Opal: an implementation science tool for machine learning clinical decision support in anesthesia.
  Journal of clinical monitoring and computing 2021 Bishara A, Wong A, Wang L, Chopra M, Fan W, Lin A, Fong N, Palacharla A, Spinner J, Armstrong R, Pletcher MJ, Lituiev D, Hadley D, Butte A
• Machine Learning Prediction of Liver Allograft Utilization From Deceased Organ Donors Using the National Donor Management Goals Registry.
  Transplantation direct 2021 Bishara AM, Lituiev DS, Adelmann D, Kothari RP, Malinoski DJ, Nudel JD, Sally MB, Hirose R, Hadley DD, Niemann CU
• Coronary artery disease detection using artificial intelligence techniques: A survey of trends, geographical differences and diagnostic features 1991-2020.
  Computers in biology and medicine 2020 Alizadehsani R, Khosravi A, Roshanzamir M, Abdar M, Sarrafzadegan N, Shafie D, Khozeimeh F, Shoeibi A, Nahavandi S, Panahiazar M, Bishara A, Beygui RE, Puri R, Kapadia S, Tan RS, Acharya UR
• Differences in clinical deterioration among three sub-phenotypes of COVID-19 patients at the time of first positive test: results from a clustering analysis.
  Intensive care medicine 2020 Data Science Collaborative Group
• Development and validation of machine learning models to predict gastrointestinal leak and venous thromboembolism after weight loss surgery: an analysis of the MBSAQIP database.
  Surgical endoscopy 2020 Nudel J, Bishara AM, de Geus SWL, Patil P, Srinivasan J, Hess DT, Woodson J
• Reduced-gravity environment hardware demonstrations of a prototype miniaturized flow cytometer and companion microfluidic mixing technology.
  Journal of visualized experiments : JoVE 2014 Phipps WS, Yin Z, Bae C, Sharpe JZ, Bishara AM, Nelson ES, Weaver AS, Brown D, McKay TL, Griffin D, Chan EY