Grants per year
Personal profile
Research Interests
Dr. Etemadi's lab works on a range of projects, including building novel wearable sensors, updating data aggregation from clinical systems (ranging from electronic medical records to ECG machines to infusion pumps) and using machine learning/AI to predict patient and health system outcomes.
Education/Academic qualification
Medicine, MD, University of California, San Francisco
… → 2016
Bioengineering, PhD, University of California, San Francisco
… → 2013
Bioengineering, PhD, University of California at Berkeley
Bioengineering, PhD, University of California, Berkeley
MD, University of California, San Francisco
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Recent external collaboration on country/territory level. Dive into details by clicking on the dots or
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AI-Enabled OB Ultrasound: Rapid Iteration on GA Algorithm
Etemadi, M., Vaidyanathan, M., Watters, A. N., Miller, E. S., Rajan, P. V. & Stetson, B. T.
Bill & Melinda Gates Foundation
7/16/21 → 8/31/23
Project: Research project
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Better Identification of Incidental Radiographic Findings - Part 2
Northwestern Memorial HealthCare
7/1/20 → 6/30/23
Project: Research project
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Wearable Technologies for Autonomous Combat Casualty Care
Georgia Institute of Technology, Office of Naval Research
2/1/21 → 1/31/22
Project: Research project
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Presymptomatic Early Warning System for COVID-19 Infection
Georgia Institute of Technology, Centers for Disease Control and Prevention
9/30/20 → 9/29/22
Project: Research project
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Fetal Age and Machine Learning Initiative Part 2 (FAMLI 2)
University of North Carolina at Chapel Hill, Bill & Melinda Gates Foundation
5/1/20 → 4/30/21
Project: Research project
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A multi-stage machine learning model for diagnosis of esophageal manometry
Kou, W., Carlson, D. A., Baumann, A. J., Donnan, E. N., Schauer, J. M., Etemadi, M. & Pandolfino, J. E., Feb 2022, In: Artificial Intelligence In Medicine. 124, 102233.Research output: Contribution to journal › Article › peer-review
1 Scopus citations -
Deep learning–based artificial intelligence model for identifying swallow types in esophageal high-resolution manometry
Kou, W., Galal, G. O., Klug, M. W., Mukhin, V., Carlson, D. A., Etemadi, M., Kahrilas, P. J. & Pandolfino, J. E., Jul 2022, In: Neurogastroenterology and Motility. 34, 7, e14290.Research output: Contribution to journal › Article › peer-review
1 Scopus citations -
Estimation of Changes in Intracardiac Hemodynamics Using Wearable Seismocardiography and Machine Learning in Patients with Heart Failure: A Feasibility Study
Shandhi, M. M. H., Fan, J., Heller, J., Etemadi, M., Klein, L. & Inan, O., 2022, (Accepted/In press) In: IEEE Transactions on Biomedical Engineering.Research output: Contribution to journal › Article › peer-review
3 Scopus citations -
Evaluation and Mitigation of Racial Bias in Clinical Machine Learning Models: Scoping Review
Huang, J., Galal, G., Etemadi, M. & Vaidyanathan, M., May 1 2022, In: JMIR Medical Informatics. 10, 5, e36388.Research output: Contribution to journal › Review article › peer-review
Open Access -
Novel Noninvasive Biosensors and Artificial Intelligence for Optimized Heart Failure Management
Neill, L., Etemadi, M., Klein, L. & Inan, O. T., Mar 2022, In: JACC: Basic to Translational Science. 7, 3P2, p. 316-318 3 p.Research output: Contribution to journal › Article › peer-review
Open Access