Andrew Bishara

Andrew Bishara, MD

Asst Professor in Residence

Biography

Research Focus
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.

Education

06/2022 - Diversity, Equity, and Inclusion Champion Training, University of California
06/2020 - Medical Informatics and Artificial Intelligence, Bakar Computational Health Sciences Institute at University of California, San Francisco (UCSF)
D.ABA., 05/2019 - Anesthesiology, University of California, San Francisco (UCSF)
MD, 05/2014 - Medicine, Harvard Medical School (HMS)
BSE, 06/2009 - Mechanical Engineering, Massachusetts Institute of Technology (MIT)

Publications

Machine Learning for the Prediction of Procedural Case Durations Developed Using a Large Multicenter Database: Algorithm Development and Validation Study.

Kendale S, Bishara A, Burns M, Solomon S, Corriere M, Mathis M

Development of a Machine Learning Model of Postoperative Acute Kidney Injury Using Non-Invasive Time-Sensitive Intraoperative Predictors.

Zamirpour S, Hubbard AE, Feng J, Butte AJ, Pirracchio R, Bishara A

Gender-based time discrepancy in diagnosis of coronary artery disease based on data analytics of electronic medical records.

Panahiazar M, Bishara AM, Chern Y, Alizadehsani R, Islam SMS, Hadley D, Arnaout R, Beygui RE

A descriptive appraisal of quality of reporting in a cohort of machine learning studies in anesthesiology.

Kothari R, Chiu C, Moukheiber M, Jehiro M, Bishara A, Lee C, Pirracchio R, Celi LA

Data Analytics of Electronic Health Records to Enhance Care of Coronary Artery Disease in Younger Women with Avoiding Possible Delay in Treatment.

Panahiazar M, Bishara AM, Chern Y, Alizadehsani R, Latif OS, Hadley D, Beygui RE

Clinical artificial intelligence quality improvement: towards continual monitoring and updating of AI algorithms in healthcare.

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.

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.

Bishara A, Maze EH, Maze M

Postoperative delirium prediction using machine learning models and preoperative electronic health record data.

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.

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.

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.

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.

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.

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.

Phipps WS, Yin Z, Bae C, Sharpe JZ, Bishara AM, Nelson ES, Weaver AS, Brown D, McKay TL, Griffin D, Chan EY