2023 News

We are excited to announce the release of Medical AI Research Foundations — a repository of open-source medical foundation models and a collaboration between Google Health and PhysioNet. Our goal in releasing this collection of resources is to accelerate medical AI research and to democratize access to foundational medical AI models.

We are seeding Medical AI Research Foundations with REMEDIS models for chest X-ray and pathology (with related Github code). We expect to add more models and resources for training medical foundation models such as datasets and benchmarks in the future. We also welcome contributions from the medical AI research community.

We have received inquiries regarding the use of credentialed data (MIMIC-III, MIMIC-IV, MIMIC-CXR) with online services such as GPT. The PhysioNet Credentialed Data Use Agreement explicitly prohibits sharing access to the data with third parties, including sending it through APIs provided by companies like OpenAI, or using it in online platforms like ChatGPT.

If you are interested in using the GPT family of models, we recommend enrolling in the Azure OpenAI service. You'll need to opt out of human review of the data, as (1) you are processing sensitive data where the likelihood of harmful outputs and/or misuse is low, and (2) you do not have the right to permit Microsoft to process the data for abuse detection due to the data use agreement you have signed. The form for opting out of the review process is available here: https://aka.ms/oai/additionalusecase

If you have any questions about this policy, feel free to reach out: https://physionet.org/about/#contact_us

The EnPRO Lab in the Department of Engineering and School of Biomedical Engineering & Imaging Sciences at King's College London is looking to fill a music-physiology data science doctoral scholarship which is now open to international applicants for October 2023 entry. The successful applicant will join the research team of the ERC COSMOS project (cosmos.isd.kcl.ac.uk).

The research investigates the impact of music expressivity on the autonomic nervous system. The project focuses on developing individualized, explanatory computational models for modulating autonomic responses through music that can be used in digital therapeutics for cardiovascular health. The scientific approach will be based on studying the interactions between musical prosody (acoustic variations introduced in musical communication) and autonomic parameters such as heart rate, heart rate variability, respiration, and blood pressure. The methods build on software tools developed in the COSMOS project.

The research activities will include study design, ethics application, data collection, data processing, computational modelling, and analysis and interpretation of results, and disseminating results through publications and conference presentations. The ideal candidate will be knowledgeable in Python, Matlab, or R, and have a Bachelors or Masters degree in biomedical engineering, mathematical and computational sciences, music information research, or a related discipline. Experience in analysis of biosignals and/or music signals, or industrial experience is desirable. 

Funding is available for 3.5 years, covers fees and stipend, and standard computing/travel support. For further details, please see the job posting.

An estimated 7 to 10 million people around the world have Parkinson’s disease, many of whom suffer from freezing of gait (FOG). FOG are unpredictable, unexpected, involuntary episodic events. During a FOG episode, patients report that their feet are inexplicably “glued” to the ground, preventing them from moving forward despite their attempts.

PhysioNet contributor Jeff Hausdorff and his colleagues at the Tel Aviv Sourasky Medical Center, KU Leuven, and Harvard Medical School, have contributed a large dataset to a machine learning contest that was recently launched to automatically detect FOG episodes and to address the shortcomings of existing methods.

This work has the potential to help advance the evaluation, understanding, and treatment of FOG, and, ultimately, to improve the lives of the many people who suffer from this debilitating Parkinson’s disease symptom. To join the competition, visit Kaggle.

Timeline

• June 1, 2023: Entry Deadline. You must accept the competition rules before this date in order to compete.
• June 1, 2023: Team Merger Deadline. This is the last day participants may join or merge teams.
• June 8, 2023: Final Submission Deadline.

Prizes

• 1st Place: $40,000
• 2nd Place: $25,000
• 3rd Place: $20,000
• 4th Place: $10,000
• 5th Place: $5,000

Over 45 students, academics, clinicians, and engineers gathered at the Google Canada offices on 23-24 February for the Toronto Health Datathon 2023. Participants used anonymized real-world data from Health Data Nexus to develop machine learning models aimed at solving real-world problems facing Canadian healthcare.

Over the past two years, PhysioNet has been collaborating with Temerty Centre for Artificial Intelligence Research and Education in Medicine (T-CAIREM) at the University of Toronto to develop the software that underpins both Health Data Nexus and PhysioNet. We look forward to continuing this collaboration, working towards tight integration between the two platforms.

We are excited to be supporting the 3rd Tokyo Datathon on Machine Learning in Healthcare, co-organized by Tokyo Medical and Dental University and MIT Critical Data. The event will be held on 1-3 September 2023 and will bring together experts from across healthcare and data science to tackle clinical questions.

Registration and event details will be posted on the Datathon Website. If you are a Japanese-language speaker with experience with the MIMIC dataset and would be interested in helping as a mentor at the event, please reach out to Leo Anthony Celi.

We are delighted to announce the opening of the George B. Moody PhysioNet Challenge 2023. This year’s Challenge invites teams to use electroencephalogram (EEG) recordings to predict the neurological recovery of patients from coma in the hours following resuscitation from cardiac arrest. This Challenge leverages a novel database of over 1,000 subjects from seven hospitals who together underwent over 50,000 hours of EEG monitoring. As always, the team with the best score for this task on the hidden test set wins the Challenge.

We have shared data, example code, and scoring code in both MATLAB and Python, and we will open the scoring system in the coming weeks. 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 and share the quirks in our data, our scoring system, and otherwise. 

Please see the Challenge website and the Challenge forum for more information, rules and deadlines: https://physionetchallenges.org/2023/
https://groups.google.com/g/physionet-challenges/

Many thanks again for your continued support of this event, and we hope that you enjoy the 2023 Challenge.

We are delighted to announce that the George B. Moody PhysioNet Challenges were awarded the "Distinguished Achievement Award for Data Reuse, as part of the DataWorks! Prize, while MIT Critical Data was awarded "Significant Achievement Award for Data Sharing". 

Launched on May 11, 2022, the Data Works! Prize was created in partnership between the NIH Office of Data Science Strategy and the Federation of American Societies for Experimental Biology (FASEB) to highlight the critical role of data sharing and reuse in scientific discovery.

George B. Moody designed and led the Challenges from 2000 to 2015. Prof. Clifford has led the Challenges since 2015 and has been a key contributor to its parent resource, PhysioNet (The Research Resource for Complex Physiologic Signals), for over two decades. Prof. Reyna has co-led the PhysioNet Challenges since 2019, and has been instrumental in the development of its repeatable science standards. 

MIT Critical Data, led by the Laboratory for Computational Physiology, builds communities around the world across disciplines to derive knowledge from data routinely collected in the process of care in order to understand health and disease better, and in the local context. Its flagship project is the Medical Information Mart for Intensive Care, or the MIMIC database.

More on the DataWorks! Prize here: https://datascience.nih.gov/director/directors-blog-dataworks-winners-2023 and the PhysioNet Challenges here: https://physionetchallenges.org/faq and MIT Critical Data here: https://criticaldata.mit.edu/.

PhysioNet and MIMIC are supported by the National Institute of Biomedical Imaging and Bioengineering.

Our colleagues at UCSF Department of Anesthesia, The UCSF Hypoxia Lab (hypoxialab.org) and the UCSF Center for Health Equity in Surgery and Anesthesia (chesa.ucsf.edu) are seeking a full-time Data Analyst (with data engineering skills) to join their Open Oximetry project.

This project seeks to understand the potential impact of skin color on accuracy of pulse oximetry and other medical diagnostic devices. The Data Analyst will work at the intersection of health diagnostics, health equity and AI in the world’s leading lab (hypoxialab.org) for this type of research along with a team of experts who have published some of the seminal research on this topic.

As part of the project, the team will be setting up robust data collection systems in the lab and the hospital settings as well as creating and managing an open access data repository for diagnostics device data. This repository will contain data from our lab as well as data from collaborating study groups. The data will be shared via portals that facilitate raw data utilization for researchers and industry, as well as visualized data to help lay persons and consumers better understand device technology performance and standards.

The Data Analyst will work closely with the laboratory-based clinical research team to gather, analyze, and interpret a wide variety of research data; Design and conduct research including selecting data, developing research instruments, analyzing collected information according to established statistical methods, and developing recommendations based on research findings; Prepare reports, charts, tables, and other visual aids to interpret and communicate data and results; Create and manage data repositories; work with our AI/ML team to test novel analytic methods with our data. The ideal candidate will be knowledgeable in SQL and Python or R and have a Bachelors or Masters degree in Statistics, Data Science, or adjacent technical field.  

This position is for a period of 12 months, though may be longer contingent on further funding. For further details, see the job posting.

We are excited to announce the launch of a shared task on problem list summarization at the BioNLP Workshop 2023. The goal for participants is to generate a list of diagnoses and problems in a patient’s daily care plan using input from the provider’s progress notes during hospitalization. The task contains 768 progress notes for training, and 300 progress notes for evaluation. The goal of this shared task is to attract future research efforts in building NLP models for real-world decision support applications, where a system generating relevant and accurate diagnoses will assist the healthcare providers’ decision-making process and improve the quality of care for patients.

Participants will be tasked with developing NLP systems for EHR summarization. Participants who design novel systems and achieve competitive performance in the shared task, running from January to April 2023, will be invited to present their results at the BioNLP Workshop, which will be held in Toronto, Canada and co-located with ACL 2023. The challenge is open to anyone interested in clinical NLP and medical AI. We encourage individuals, teams, and organizations to participate.

To register for the challenge, please visit: https://forms.gle/geTXN6Z1pyfC55Fn8. More information about the challenge, including the official rules and guidelines, can be found at: https://physionet.org/content/bionlp-workshop-2023-task-1a/. You are welcome to join our google discussion group for newest update: https://groups.google.com/g/bionlp2023problemsumm

The SOAP Note Tagging and Problem List Summarization dataset dataset is temporarily unavailable as it is part of an ongoing shared task of BioNLP Workshop 2023: 1A (Problem List Summarization). The dataset will be made available on July 13th, 2023. More details about the workshop and shared task can be found at: https://doi.org/10.13026/s8wk-ja78

We apologize for any inconvenience this may cause and appreciate your understanding. We will provide updates as soon as more information becomes available. A new test set with 300 progress notes will be released along with the original set of 768 notes when the embargo is lifted. If you are interested in signing up the shared task, register here: https://forms.gle/geTXN6Z1pyfC55Fn8