2024 News

We recognize that there is value in creating datasets or models that are either derived from MIMIC or which augment MIMIC in some way (for example, by adding annotations). Here are some guidelines on creating these datasets and models:

• Any derived datasets or models should be treated as containing sensitive information. If you wish to share these resources, they should be shared on PhysioNet under the same agreement as the source data.
• If you would like to use the MIMIC acronym in your project name, please include the letters “Ext” (for example, MIMIC-IV-Ext-YOUR-DATASET"). Ext may either indicate “extracted” (e.g. a derived subset) or “extended” (e.g. annotations), depending on your use case.

We are currently experiencing network issues at MIT, impacting the availability of PhysioNet services. We apologize for any inconvenience this may cause and are working to resolve the issue as soon as we can.

We are delighted to announce that the George B. Moody PhysioNet Challenges are partnering with Data Science Africa (DSA) and the IEEE Signal Processing Society's Challenges and Data Collections Committee (CDCC). The IEEE CDCC is supporting this year’s Challenge with additional cash prizes for participating teams from Africa and the Challenge organizers will be running a workshop at this year's annual DSA meeting in Kenya. In connection with this, the Challenge organizers will be running a workshop at DSA in Kenya from June 2-5th 2024. Please note that we are also accepting (and scoring) entries, and there are two deadlines coming up - April 8th to submit a preliminary entry to the Challenge and April 15th to submit a (placeholder) abstract to CinC.

Our colleagues at Duke are hosting a Critical Care Datathon on April 13-14, 2024. The Datathon is a collaborative two-day event that connects critical care clinicians with data scientists to develop pragmatic data-driven models using de-identified critical care electronic health record datasets.

Using de-identified critical care electronic health record datasets (including MIMIC and the eICU Collaborative Research Database), participants will develop new projects in 36 hours, from problem to abstract (and more)! 

Participants will be organized into teams that are half-data science, half-clinical. You do not need to have a team; the organizers will help you find a team. Questions will be crowdsourced. No experience is required.

• If you are a clinician, your interest, but not expertise, in data science is required. 
• If you are a data scientist, your interest, but not expertise, in healthcare and critical care is required. 

For more information, see: https://sites.duke.edu/datathon2024/

The 2024 Conference on Health, Inference, and Learning (CHIL) invites submissions focused on artificial intelligence and machine learning (AI/ML) techniques that address challenges in health, which includes clinical healthcare, public health, health economics, informatics, and more. For full details, refer to the online Call for Papers: https://www.chilconference.org/call-for-papers.html 

This year, CHIL 2024 will accept submissions for three distinct tracks: Models and Methods, Applications and Practice, and Policy, Impact and Society. Accepted papers will be published in the Proceedings of Machine Learning Research (PMLR). We are also offering Best Paper Awards to recognize outstanding work across all tracks.

The deadline for submissions has been extended to: Friday, 16 Feb 2024 at 11:59pm AoE. Submit your paper at: https://openreview.net/group?id=chilconference.org/CHIL/2024/Conference

The 2024 Conference on Health, Inference, and Learning (CHIL) invites submissions focused on artificial intelligence and machine learning (AI/ML) techniques that address challenges in health, which includes clinical healthcare, public health, health economics, informatics, and more. For full details, refer to the online Call for Papers: https://www.chilconference.org/call-for-papers.html 

This year, CHIL 2024 will accept submissions for three distinct tracks: Models and Methods, Applications and Practice, and Policy, Impact and Society. Accepted papers will be published in the Proceedings of Machine Learning Research (PMLR). We are also offering Best Paper Awards to recognize outstanding work across all tracks.

Submissions are due on February 5th, 11:59 PM EST in the form of anonymized PDF files. All submissions for CHIL 2024 will be managed through the OpenReview system. Similar to last year, we have a full author response period and reviewer discussion period to ensure proper feedback on the work. 

Hosted by The Association of Health, Learning, and Inference (AHLI), the CHIL conferences have consistently served as premier scientific meetings, uniting clinicians and researchers from both industry and academia, and weaving a rich tapestry of knowledge and innovation.

Building on a series of conferences and events since 2019, CHIL has persistently set a benchmark in interdisciplinary research within the realms of machine learning and health, demonstrated through its impactful sessions (2020, 2021, 2022, 2023). Following the resounding success of CHIL 2023 at the Broad Institute, Cambridge, we are thrilled to announce that CHIL 2024 will continue fostering insightful discussions and collaborations in the field. The 5th annual conference will take place in-person from June 27-28 at the Verizon Executive Education Center at Cornell Tech in New York City. 

Important Dates

• Submissions due: Feb 5, 2024 at 11:59pm
• Bidding opens for reviewers: Feb 6, 2024 at 11:59pm
• Reviews released: Mar 4, 2024 by 11:59pm
• Author/Reviewer discussion period: Mar 10-21, 2024
• Author notification: Apr 3, 2024 by 11:59pm
• CHIL conference: June 27-28, 2024

We are delighted to announce the opening of the George B. Moody PhysioNet Challenge 2024. The 2024 Challenge invites teams to develop algorithms for digitizing and classifying electrocardiograms (ECGs) captured from images or paper printouts.

Despite recent advances in digital ECG devices, paper or physical ECGs remain common, especially in the Global South. These paper ECGs document the history and diversity of cardiovascular diseases (CVDs), and algorithms that can digitize and classify these images have the potential to improve our understanding and treatment of CVDs, especially for underrepresented and underserved populations.

We have shared example code and scoring code in both MATLAB and Python and synthetic ECG generation code in Python. While last year’s Challenge had the largest dataset yet, this year’s Challenge begins with a much more tractable dataset that you may already have on your machine, and you can use the provided code to create ECG images with realistic artifacts. We will also augment these data to create a much richer and more representative dataset, so stay tuned for more announcements. We will open the scoring system in the coming days.

See the Challenge website for more information, rules and deadlines: https://physionetchallenges.org/2024/

As in previous years, we have divided the Challenge into two phases: an unofficial phase and an official phase. The unofficial phase solicits feedback from the research community (i.e., you) to help us to improve the Challenge for the official phase, so we require teams to register and participate in the unofficial phase of the Challenge to be eligible for a prize. Please enter early and often – we need you to look for quirks in our data, our scoring system, and otherwise. We are imperfect (and bandwidth-limited), so please send us suggestions via the forum (see below). We rely on the community to help us to improve the quality of the Challenge each year.

More information will be posted on the PhysioNet Challenge website and the Challenge forum as it becomes available. Please post questions and comments to the Challenge forum as well. However, if your question reveals information about your entry, then please email info [at] physionetchallenge.org instead to help us safeguard the diversity of approaches to the Challenge. We may post parts of our replies publicly if we feel that all Challengers should benefit from the information contained in our responses. We will not answer emails about the Challenge sent to other email addresses.

Many thanks again for your continued support of this event, and we hope that you enjoy the 2024 Challenge!

On behalf of our colleagues, we are pleased to announce a call for papers for a focus collection in IOP Physiological Measurement on the topic of "Open Source and Validated Computational Tools for Physiological Time Series Analysis".

Physiological time series analysis plays a crucial role in understanding the complex dynamics of biological systems and their response to stimuli and interventions. The availability of reliable, open-source computational tools is essential for advancing research in this field, facilitating reproducibility, promoting collaboration, and accelerating scientific discoveries.

This focus collection aims to showcase the latest advancements in open-source tools and methodologies that have been rigorously validated for the analysis of physiological time series data.

Guest Editors

• Joachim A. Behar, Technion Institute of Technology, Israel
• Peter H. Charlton, University of Cambridge, UK
• Márton Áron Goda, Technion Institute of Technology, Israel
• Maarten De Vos, KU Leuven, Belgium

For questions, please contact Dr. Joachim A. Behar (jbehar@technion.ac.il).