The IEEE-EMBS International Conference on Biomedical and Health Informatics (BHI), sponsored by the IEEE Engineering in Medicine and Biology Society (IEEEEMBS), is EMBS’s primary technical conference on informatics and computing in healthcare and life sciences. BHI 2023 will take place in Pittsburgh, Pennsylvania from October 15-18, 2023. It will provide a unique forum to showcase basic and translational research on big data analytics and machine learning that address challenges in the acquisition, transmission, processing, security, visualization, and interpretation of vast volumes of multi-modal biomedical data, as well as related social, behavioral, environmental, and geographical data. It will also demonstrate the deployment of BHI informatics solutions that integrate key technologies including artificial intelligence, machine learning, mHealth, e-Health, human-computer interface, telemedicine, bioinformatics, sensors, imaging, and public health monitoring, to achieve patient-centric and outcome-driven effective health care.
- Open Access: BHI 2023 proudly feature Open-Access publishing for accepted regular and late-breaking papers.
- Travel Awards: for students, trainees, and postdocs are made available through a National Science Foundation (NSF) grant.
- JBHI Special Issue: Top-rated regular papers will be recommended for a Special Issues IEEE Journal of Biomedical and Health Informatics (IF 7.02).
- Late Breaking Papers: In line with our commitment to fostering the latest advancements and disseminating cutting-edge research, we invite you to submit your late-breaking papers to BHI 2023.
Exciting Workshops, Special Sessions, & Tutorials
- (W) Computational Medicine Leveraging High Performance Computing and Artificial Intelligence.
- (W) Addressing real-world needs for digital health care: current and future challenges of the “human centered” design.
- (SS) Multimodal Learning in Healthcare: From Wearable Sensing to Clinical AI Decision-Making.
- (SS) Trustworthy and responsible data analytics for mental health.
- (T) From Few to None: Exploring Few-Shot, One-Shot, and Zero-Shot Deep Learning in Clinical Settings.
