Rashidi, Parisa

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

Shickel, B., Tighe, P. J., Bihorac, A., & Rashidi, P. (2018) · IEEE Journal of Biomedical and Health Informatics

Machine Learning in HealthcareElectronic Health Records Systems

Cited by 1,688

A survey on ambient-assisted living tools for older adults

Rashidi, P., & Mihailidis, A. (2013) · IEEE Journal of Biomedical and Health Informatics

Context-Aware Activity Recognition SystemsTechnology Use by Older Adults

Cited by 1,128

Artificial Intelligence and Surgical Decision-making.

Loftus, T. J., Tighe, P. J., Filiberto, A. C., Efron, P. A., Brakenridge, S. C., Mohr, A. M., Rashidi, P., Upchurch, G. R., & Bihorac, A. (2020) · JAMA Surgery

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 650

The Behavioral Intervention Technology Model: An Integrated Conceptual and Technological Framework for eHealth and mHealth Interventions

Mohr, D. C., Schueller, S. M., Montague, E., Burns, M. N., & Rashidi, P. (2014) · Journal of Medical Internet Research

Mobile Health and mHealth ApplicationsDigital Mental Health Interventions

Cited by 510

Discovering Activities to Recognize and Track in a Smart Environment

Rashidi, P., Cook, D. J., Holder, L. B., & Schmitter-Edgecombe, M. (2011) · IEEE Transactions on Knowledge and Data Engineering

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 462

Keeping the Resident in the Loop: Adapting the Smart Home to the User

Rashidi, P., & Cook, D. J. (2009) · IEEE Transactions on Systems, Man, and Cybernetics - Part A: Systems and Humans

Context-Aware Activity Recognition SystemsIoT-based Smart Home Systems

Cited by 443

MySurgeryRisk: development and validation of a machine-learning risk algorithm for major complications and death after surgery.

Bihorac, A., Ozrazgat-Baslanti, T., Ebadi, A., Motaei, A., Madkour, M., Pardalos, P. M., Lipori, G., Hogan, W. R., Efron, P. A., Moore, F., et al. (2019) · Annals of Surgery

Cardiac, Anesthesia and Surgical OutcomesMachine Learning in Healthcare

Cited by 377

Human Activity Recognition Using Inertial, Physiological and Environmental Sensors: A Comprehensive Survey

Demrozi, F., Pravadelli, G., Bihorac, A., & Rashidi, P. (2020) · IEEE Access

Context-Aware Activity Recognition SystemsGait Recognition and Analysis

Cited by 368

Accessing Artificial Intelligence for Clinical Decision-Making

Giordano, C., Brennan, M., Mohamed, B., Rashidi, P., Modave, F., & Tighe, P. (2021) · Frontiers in Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 326

Successful aging: Advancing the science of physical independence in older adults

Anton, S. D., Woods, A. J., Ashizawa, T., Barb, D., Buford, T. W., Carter, C. S., Clark, D. J., Cohen, R. A., Corbett, D. B., Cruz-Almeida, Y., et al. (2015) · Ageing Research Reviews

Balance, Gait, and Falls PreventionAging and Gerontology Research

Cited by 292

A smartwatch-based framework for real-time and online assessment and mobility monitoring

Kheirkhahan, M., Nair, S., Davoudi, A., Rashidi, P., Wanigatunga, A. A., Corbett, D. B., Mendoza, T., Manini, T. M., & Ranka, S. (2019) · Journal of Biomedical Informatics

Context-Aware Activity Recognition SystemsMobile Health and mHealth Applications

Cited by 138

Improved predictive models for acute kidney injury with IDEA: Intraoperative Data Embedded Analytics.

Adhikari, L., Ozrazgat-Baslanti, T., Ruppert, M., Madushani, R. W. M. A., Paliwal, S., Hashemighouchani, H., Zheng, F., Tao, M., Lopes, J. M., Li, X., et al. (2019) · PLOS ONE

Acute Kidney Injury ResearchArtificial Intelligence in Healthcare

Cited by 98

COM: A method for mining and monitoring human activity patterns in home-based health monitoring systems

Rashidi, P., & Cook, D. J. (2013) · ACM Transactions on Intelligent Systems and Technology

Context-Aware Activity Recognition SystemsHuman Mobility and Location-Based Analysis

Cited by 94

Improving the Intensive Care Patient Experience With Virtual Reality—A Feasibility Study

Ong, T. L., Ruppert, M. M., Akbar, M., Rashidi, P., Ozrazgat-Baslanti, T., Bihorac, A., & Suvajdzic, M. (2020) · Critical Care Explorations

Intensive Care Unit Cognitive DisordersVirtual Reality Applications and Impacts

Cited by 80

Uncertainty-aware deep learning in healthcare: A scoping review

Loftus, T. J., Shickel, B., Ruppert, M. M., Balch, J. A., Ozrazgat-Baslanti, T., Tighe, P. J., Efron, P. A., Hogan, W. R., Rashidi, P., Upchurch, G. R., et al. (2022) · PLOS Digital Health

Artificial Intelligence in Healthcare and EducationMachine Learning in Healthcare

Cited by 73

Machine Learning Applications in Solid Organ Transplantation and Related Complications

Balch, J. A., Delitto, D., Tighe, P. J., Zarrinpar, A., Efron, P. A., Rashidi, P., Upchurch, G. R., Bihorac, A., & Loftus, T. J. (2021) · Frontiers in Immunology

Artificial Intelligence in Healthcare and EducationTransplantation: Methods and Outcomes

Cited by 33

Predictive Modeling for Readmission to Intensive Care: A Systematic Review

Ruppert, M. M., Loftus, T. J., Small, C., Li, H., Ozrazgat-Baslanti, T., Balch, J., Holmes, R., Tighe, P. J., Upchurch, G. R., Efron, P. A., et al. (2023) · Critical Care Explorations

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 24

Deep Multi-Modal Transfer Learning for Augmented Patient Acuity Assessment in the Intelligent ICU

Shickel, B., Davoudi, A., Ozrazgat-Baslanti, T., Ruppert, M., Bihorac, A., & Rashidi, P. (2021) · Frontiers in Digital Health

Machine Learning in HealthcareSepsis Diagnosis and Treatment

Cited by 21

Promoting AI Competencies for Medical Students: A Scoping Review on Frameworks, Programs, and Tools

Ma, Y., Song, Y., Balch, J. A., Ren, Y., Vellanki, D., Hu, Z., Brennan, M., Kolla, S., Guan, Z., Armfield, B., et al. (2024) · arXiv

Artificial Intelligence in Healthcare and EducationInnovations in Medical Education

Cited by 16

Physiologic signatures within six hours of hospitalization identify acute illness phenotypes

Ren, Y., Loftus, T. J., Li, Y., Guan, Z., Ruppert, M. M., Datta, S., Upchurch, G. R., Tighe, P. J., Rashidi, P., Shickel, B., et al. (2022) · PLOS Digital Health

Sepsis Diagnosis and TreatmentMachine Learning in Healthcare

Cited by 6