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Biography

Dr. Parisa Rashidi received her PhD in computer science with an emphasis on machine learning. She is currently an assistant professor at the J. Crayton Pruitt Family Department of Biomedical Engineering (BME) at University of Florida (UF). She is also affiliated with the Electrical & Computer Engineering (ECE), as well as Computer & Information Science & Engineering (CISE) departments. She is the director of the “Intelligent Health Lab” (i-Heal). Her area of research includes developing machine learning and artificial intelligence methods for solving healthcare problems. More specifically, she is working on: (1) transforming patient care in the Intensive Care Unit by developing autonomous monitoring tools using advanced machine learning techniques, (2) Developing intelligent tools for monitoring cognitive and mental conditions of community-dwelling patients. She has forged a highly collaborative research program across campus to address these problems, collaborating with several departments at College of Medicine including nephrology, anesthesiology, clinical neuropsychology, and aging.

Featured Research Projects

  • Intelligent ICU

    Our long-term goal is to sense, quantify, and communicate patient condition in an autonomous, precise, and personalized manner. We plan to build the foundation of an intelligent ICU by developing and validating pervasive context sensing, precise context inference, and situation-aware context communication using novel sensing and artificial intelligence technologies. This project is carried out at Intensive Care Units (ICU) at UF Health hospitals (UFH).

  • AI & Mental Health

    The goal of this project is to develop  natural language processing and machine learning techniques to provide personalized treatments to customize and individualize online mental health treatment, the Intelligent Counseling System (ICS).

  • In-Community Health Monitoring

    Our long term goal is to develop an ecologically momentary assessment tool for capturing patient status in real time in the context of momentary physiologic and psychometric data.

Featured Publications

The Intelligent ICU Pilot Study: Using Artificial Intelligence Technology for Autonomous Patient Monitoring

Preprint
Anis Davoudi, Kumar Rohit Malhotra, Benjamin Shickel, Scott Siegel, Seth Williams, Matthew Ruppert, Emel Bihorac, Tezcan Ozrazgat-Baslanti, Patrick J. Tighe, Azra Bihorac, Parisa Rashidi
arXiv:1804.10201
Publication year: 2018

DeepSOFA: A Real-Time Continuous Acuity Score Framework using Deep Learning

Preprint
Benjamin Shickel, Tyler J Loftus, Tezcan Ozrazgat-Baslanti, Ashkan Ebadi, Azra Bihorac, Parisa Rashidi
arXiv preprint arXiv:1802.10238
Publication year: 2018

Deep EHR: A Survey of Recent Advances in Deep Learning Techniques for Electronic Health Record (EHR) Analysis

Journal Article
Benjamin Shickel, Patrick James Tighe, Azra Bihorac, Parisa Rashidi
IEEE Journal of Biomedical and Health Informatics
Publication year: 2018

Honors and Awards

  • 2018
    National Science Foundation Faculty Early Career Development Program (NSF CAREER)
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    This award will allow to advance exploration of machine learning algorithms and critical care medicine. Precise assessment and prediction of patient status in the ICU can enable early interventions and can result in improved patient outcomes. However, today’s ICUs still face many barriers for assessing and predicting patient status. Essential information such as pain and functional status are not captured automatically, but rather are repetitively measured by ICU nurses and existing methods have limited accuracy and infrequently used. This leads to missing opportunities for early interventions. This will represent the first attempt to autonomously assess pain and functional status in the ICU, to predict precise patient trajectory from high-resolution data, and to improve predictive clinical models through user feedback. In addition, the research will contribute to a broader understanding of future design considerations for the next generation of lifelong learning systems and intelligent hospitals.
  • 2017
    National Academy of Engineering (NAE), Frontiers of Engineering
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    The Frontiers of Engineering program brings together a select group of emerging engineering leaders from industry, academe, and government labs to discuss pioneering technical work and leading edge research in various engineering fields and industry sectors.
  • 2015
    Biomedical Engineering Society (BMES) Career Development Award
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  • 2015
    Microsoft Faculty
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    Invited Summit Participant
  • 2014
    National Science Foundation Travel Award
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    Computing Challenges in Future Mobile Health Systems and Applications Workshop
  • 2011
    The Outstanding Dissertation Award
    Washington State University, WA
  • 2008
    Graduate Research Award

Education

  • PhD January 2008 - May 2011

    PhD in Computer Science - Applied Machine Learning

    Washington State University

  • M.Sc. September 2006 - December 2007

    M.Sc. in Computer Science - Applied Machine Learning

    Washington State University

  • B.Sc. September 2000 - September 2005

    B.Sc. in Computer Engineering

    University of Tehran

Positions

  • presentAugust 2013

    Assistant Professor

    University of Florida, Biomedical Engineering

  • present2013

    Affiliated Assistant Professor

    University of Florida, Electrical & Computer Engineering

  • present2016

    Affiliated Assistant Professor

    University of Florida, Computer & Information Science & Engineering

  • June 2013September 2012

    Assistant Professor

    Feinberg School of Medicine, Northwestern University